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Page 1: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

Introduction to the Oracle Stream ExplorerFast Data

ORAC L E W H I T E

Introduction to the Oracle Stream ExplorerFast Data

ORAC L E W H I T E

Introduction to the Oracle Stream ExplorerFast Data and Event Processing

ORAC L E W H I T E P AP E R | M ARCH 2 0 1 5

Introduction to the Oracle Stream Explorerand Event Processing

MARCH 2 0 1 5

Introduction to the Oracle Stream Explorerand Event Processing without Software Coding

Introduction to the Oracle Stream Explorerwithout Software Coding

Introduction to the Oracle Stream Explorerwithout Software Coding

Introduction to the Oracle Stream Explorer without Software Coding

Page 2: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

1

Table of Contents

Introduction

Understanding Shapes, Streams, References and Explorations

Case Stu

Conclusion

Appendix A: Scripts and Samples used in the Case Study

Appendix B: Creating a Message

1 | INTRODUCTION TO THE

Table of Contents

Introduction

Understanding Shapes, Streams, References and Explorations

Shapes

Streams and References

Explorations and Patterns

Case Study: Implementing the Minority Report Mall Scene

Part One: Creating the Solution Design for the Scenario

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Conclusion

Appendix A: Scripts and Samples used in the Case Study

Appendix B: Creating a Message

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Table of Contents

Understanding Shapes, Streams, References and Explorations

Streams and References

Explorations and Patterns

dy: Implementing the Minority Report Mall Scene

Part One: Creating the Solution Design for the Scenario

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Appendix A: Scripts and Samples used in the Case Study

Appendix B: Creating a Message

ORACLE STREAM EXPLORER

Understanding Shapes, Streams, References and Explorations

Streams and References

Explorations and Patterns

dy: Implementing the Minority Report Mall Scene

Part One: Creating the Solution Design for the Scenario

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Appendix A: Scripts and Samples used in the Case Study

Appendix B: Creating a Message-Driven Bean that Greets

Understanding Shapes, Streams, References and Explorations

dy: Implementing the Minority Report Mall Scene

Part One: Creating the Solution Design for the Scenario

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Appendix A: Scripts and Samples used in the Case Study

Driven Bean that Greets

Understanding Shapes, Streams, References and Explorations

dy: Implementing the Minority Report Mall Scene

Part One: Creating the Solution Design for the Scenario

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Appendix A: Scripts and Samples used in the Case Study

Driven Bean that Greets

Understanding Shapes, Streams, References and Explorations

Part Two: Implementing the Artifacts in Oracle Stream Explorer

2

3

3

3

4

6

7

9

23

23

29

Page 3: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

2

Introduction

Events are everywhere. From the moment we wake up until t

without our knowledge. According to any popular dictionary, an event is something that just happens. It

can be a thermostat being adjusted by a ho

store, or a car

analysis can be viewed as an event, and

events is important because they tell us what is going on and provide us with awareness of the current

situation around us and in the wider world.

The technique of analyzing event relationships and their consequences is called event processing.

Event proc

take action,

highway

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

unnoticed.

twenty

Most people think that many, if not all, industries already use event processing somehow, but the

reality is that few of them are

to allow the exchange of events between different systems. Bu

characteristics

place, only event delivery. Just to name a few,

of industries using event processing. But those are event

is part of their core business. Why then, i

failing to leverage it?

The main reason is likely the level of abstraction of current technologies. When compared to business

intelligence, event processing technologies offers a low level of

event processing

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

processing app

capabilities. But this

to the person interested in doing the analysis.

2 | INTRODUCTION TO THE

Introduction

Events are everywhere. From the moment we wake up until t

without our knowledge. According to any popular dictionary, an event is something that just happens. It

can be a thermostat being adjusted by a ho

store, or a car

analysis can be viewed as an event, and

events is important because they tell us what is going on and provide us with awareness of the current

situation around us and in the wider world.

The technique of analyzing event relationships and their consequences is called event processing.

Event processing handles events while they are still in motion because that is the perfect moment to

take action, like paying attention to a low fuel warning light on your car to avoid running out of gas on a

highway. Once events happen, they become past tense and t

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

unnoticed. For this reason, event processing has never been as important as it is now, especially for

twenty-first-century enterprises.

Most people think that many, if not all, industries already use event processing somehow, but the

reality is that few of them are

to allow the exchange of events between different systems. Bu

characteristics

place, only event delivery. Just to name a few,

of industries using event processing. But those are event

is part of their core business. Why then, i

failing to leverage it?

The main reason is likely the level of abstraction of current technologies. When compared to business

intelligence, event processing technologies offers a low level of

event processing

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

processing app

capabilities. But this

to the person interested in doing the analysis.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Introduction

Events are everywhere. From the moment we wake up until t

without our knowledge. According to any popular dictionary, an event is something that just happens. It

can be a thermostat being adjusted by a ho

store, or a car passing through

analysis can be viewed as an event, and

events is important because they tell us what is going on and provide us with awareness of the current

situation around us and in the wider world.

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

Once events happen, they become past tense and t

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

century enterprises.

Most people think that many, if not all, industries already use event processing somehow, but the

reality is that few of them are

to allow the exchange of events between different systems. Bu

characteristics such as loose coupling and message routing; there is no actual event pr

place, only event delivery. Just to name a few,

of industries using event processing. But those are event

is part of their core business. Why then, i

failing to leverage it?

The main reason is likely the level of abstraction of current technologies. When compared to business

intelligence, event processing technologies offers a low level of

event processing technologies like

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

processing applications; that include extreme low latency and high throughput, just like

capabilities. But this comes with a high price, which is exposing the technical details of the technology

to the person interested in doing the analysis.

ORACLE STREAM EXPLORER

Events are everywhere. From the moment we wake up until t

without our knowledge. According to any popular dictionary, an event is something that just happens. It

can be a thermostat being adjusted by a ho

passing through an automated toll station on a highway. Virtually everything in the final

analysis can be viewed as an event, and

events is important because they tell us what is going on and provide us with awareness of the current

situation around us and in the wider world.

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

Once events happen, they become past tense and t

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

century enterprises.

Most people think that many, if not all, industries already use event processing somehow, but the

reality is that few of them are actually using it. Most enterprises use EDA (Event

to allow the exchange of events between different systems. Bu

such as loose coupling and message routing; there is no actual event pr

place, only event delivery. Just to name a few,

of industries using event processing. But those are event

is part of their core business. Why then, i

The main reason is likely the level of abstraction of current technologies. When compared to business

intelligence, event processing technologies offers a low level of

technologies like OEP (Oracle Event Processing) are focused on the developer

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

s; that include extreme low latency and high throughput, just like

comes with a high price, which is exposing the technical details of the technology

to the person interested in doing the analysis.

Events are everywhere. From the moment we wake up until t

without our knowledge. According to any popular dictionary, an event is something that just happens. It

can be a thermostat being adjusted by a homeowner, a cre

an automated toll station on a highway. Virtually everything in the final

analysis can be viewed as an event, and many types of events may be related. Paying attention to

events is important because they tell us what is going on and provide us with awareness of the current

situation around us and in the wider world.

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

Once events happen, they become past tense and t

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

Most people think that many, if not all, industries already use event processing somehow, but the

actually using it. Most enterprises use EDA (Event

to allow the exchange of events between different systems. Bu

such as loose coupling and message routing; there is no actual event pr

place, only event delivery. Just to name a few, Automat

of industries using event processing. But those are event

is part of their core business. Why then, if event processing is

The main reason is likely the level of abstraction of current technologies. When compared to business

intelligence, event processing technologies offers a low level of

OEP (Oracle Event Processing) are focused on the developer

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

s; that include extreme low latency and high throughput, just like

comes with a high price, which is exposing the technical details of the technology

to the person interested in doing the analysis.

Events are everywhere. From the moment we wake up until the next day, millions of events

without our knowledge. According to any popular dictionary, an event is something that just happens. It

meowner, a credit card being processed at the

an automated toll station on a highway. Virtually everything in the final

types of events may be related. Paying attention to

events is important because they tell us what is going on and provide us with awareness of the current

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

Once events happen, they become past tense and therefore become nothing more than a

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

Most people think that many, if not all, industries already use event processing somehow, but the

actually using it. Most enterprises use EDA (Event

to allow the exchange of events between different systems. But despite the usage of inherent

such as loose coupling and message routing; there is no actual event pr

utomated Trading and

of industries using event processing. But those are event-driven industries by nature; event processing

f event processing is that important, are other industries still

The main reason is likely the level of abstraction of current technologies. When compared to business

intelligence, event processing technologies offers a low level of abstraction. Even the most powerful

OEP (Oracle Event Processing) are focused on the developer

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

s; that include extreme low latency and high throughput, just like

comes with a high price, which is exposing the technical details of the technology

he next day, millions of events

without our knowledge. According to any popular dictionary, an event is something that just happens. It

dit card being processed at the

an automated toll station on a highway. Virtually everything in the final

types of events may be related. Paying attention to

events is important because they tell us what is going on and provide us with awareness of the current

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

herefore become nothing more than a

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

Most people think that many, if not all, industries already use event processing somehow, but the

actually using it. Most enterprises use EDA (Event-

t despite the usage of inherent

such as loose coupling and message routing; there is no actual event pr

rading and Online Gaming

driven industries by nature; event processing

important, are other industries still

The main reason is likely the level of abstraction of current technologies. When compared to business

abstraction. Even the most powerful

OEP (Oracle Event Processing) are focused on the developer

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

s; that include extreme low latency and high throughput, just like

comes with a high price, which is exposing the technical details of the technology

he next day, millions of events happen

without our knowledge. According to any popular dictionary, an event is something that just happens. It

dit card being processed at the grocery

an automated toll station on a highway. Virtually everything in the final

types of events may be related. Paying attention to

events is important because they tell us what is going on and provide us with awareness of the current

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

herefore become nothing more than a

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

Most people think that many, if not all, industries already use event processing somehow, but the

-Driven Architecture)

t despite the usage of inherent

such as loose coupling and message routing; there is no actual event processing in

aming are examples

driven industries by nature; event processing

important, are other industries still

The main reason is likely the level of abstraction of current technologies. When compared to business

abstraction. Even the most powerful

OEP (Oracle Event Processing) are focused on the developer

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

s; that include extreme low latency and high throughput, just like fault-tolerant

comes with a high price, which is exposing the technical details of the technology

happen

without our knowledge. According to any popular dictionary, an event is something that just happens. It

grocery

an automated toll station on a highway. Virtually everything in the final

types of events may be related. Paying attention to

events is important because they tell us what is going on and provide us with awareness of the current

The technique of analyzing event relationships and their consequences is called event processing.

essing handles events while they are still in motion because that is the perfect moment to

like paying attention to a low fuel warning light on your car to avoid running out of gas on a

herefore become nothing more than a

record of a fact. Analyzing historical facts still has its value, but depending of the events that occurred

and the situation those events represented, it may signify a missed opportunity or a threat that went

For this reason, event processing has never been as important as it is now, especially for

Most people think that many, if not all, industries already use event processing somehow, but the

Driven Architecture)

t despite the usage of inherent EDA

ocessing in

examples

driven industries by nature; event processing

important, are other industries still

The main reason is likely the level of abstraction of current technologies. When compared to business

abstraction. Even the most powerful

OEP (Oracle Event Processing) are focused on the developer

community. In this context, OEP offers a complete set of tools to create, test, debug and deploy event

tolerant

comes with a high price, which is exposing the technical details of the technology

Page 4: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

3

There is a clear nee

need to understand all the technical details that can confuse business users. This was the reason

Oracle launched

that allows the creation of event processing applications through an intuitive

Oracle Stream Explorer is a web

SaaS in the Oracle Cloud, ca

analyzing streams of events in real

The goal of this paper is to provide the basic information necessary to start building applications using

Oracle Stream Explorer. It will provid

step

Understanding Shapes, Streams, References and Explorations

This section will be focused in providing a

development of event processing applications. It is highly recommended that first time users working with Oracle

Stream Explorer spend some time reading this section before venturing int

Shapes

According to many p

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

wedges, circles or

even when they are observed individually, geons also mean something because in the human brain, every geon is

matched against a structural representation of these o

shapes.

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

it has a structural representation. Building event processing applic

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

about a data set. It has a name, a list of attributes a

associated with a

each source

never pers

finishes all the data is discarded.

Streams and References

Events in general can be classified according to their temporal state. There is previously processed data that

represent facts from

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

the events happening right now, there is not eno

3 | INTRODUCTION TO THE

There is a clear nee

need to understand all the technical details that can confuse business users. This was the reason

Oracle launched

that allows the creation of event processing applications through an intuitive

Oracle Stream Explorer is a web

SaaS in the Oracle Cloud, ca

analyzing streams of events in real

The goal of this paper is to provide the basic information necessary to start building applications using

Oracle Stream Explorer. It will provid

step-by-step how to develop a sample application based on an interesting case study.

Understanding Shapes, Streams, References and Explorations

This section will be focused in providing a

development of event processing applications. It is highly recommended that first time users working with Oracle

Stream Explorer spend some time reading this section before venturing int

Shapes

According to many p

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

wedges, circles or

even when they are observed individually, geons also mean something because in the human brain, every geon is

matched against a structural representation of these o

shapes.

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

it has a structural representation. Building event processing applic

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

about a data set. It has a name, a list of attributes a

associated with a

each source type is implemented through an

never persisted. During the analysis of

finishes all the data is discarded.

Streams and References

Events in general can be classified according to their temporal state. There is previously processed data that

represent facts from

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

the events happening right now, there is not eno

INTRODUCTION TO THE ORACLE STREAM EXPLOR

There is a clear need in the

need to understand all the technical details that can confuse business users. This was the reason

Oracle launched the new Oracle Stream Explorer

that allows the creation of event processing applications through an intuitive

Oracle Stream Explorer is a web

SaaS in the Oracle Cloud, ca

analyzing streams of events in real

The goal of this paper is to provide the basic information necessary to start building applications using

Oracle Stream Explorer. It will provid

step how to develop a sample application based on an interesting case study.

Understanding Shapes, Streams, References and Explorations

This section will be focused in providing a

development of event processing applications. It is highly recommended that first time users working with Oracle

Stream Explorer spend some time reading this section before venturing int

According to many psychologists

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

wedges, circles or rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

matched against a structural representation of these o

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

it has a structural representation. Building event processing applic

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

about a data set. It has a name, a list of attributes a

associated with a source type. A source type

type is implemented through an

isted. During the analysis of

finishes all the data is discarded.

Streams and References

Events in general can be classified according to their temporal state. There is previously processed data that

represent facts from the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

the events happening right now, there is not eno

ORACLE STREAM EXPLORER

d in the industry for a product

need to understand all the technical details that can confuse business users. This was the reason

Oracle Stream Explorer

that allows the creation of event processing applications through an intuitive

Oracle Stream Explorer is a web-enabled application available to be used both on

SaaS in the Oracle Cloud, capable of providing a zero

analyzing streams of events in real-time.

The goal of this paper is to provide the basic information necessary to start building applications using

Oracle Stream Explorer. It will provide a solid introduction to the main product features and will show

step how to develop a sample application based on an interesting case study.

Understanding Shapes, Streams, References and Explorations

This section will be focused in providing a solid introduction to the most important artifacts created during the

development of event processing applications. It is highly recommended that first time users working with Oracle

Stream Explorer spend some time reading this section before venturing int

sychologists, when human beings start learning something new, they mentally break down

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

matched against a structural representation of these o

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

it has a structural representation. Building event processing applic

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

about a data set. It has a name, a list of attributes a

A source type determines

type is implemented through an OEP adapter. Another important aspect about shapes is that they are

isted. During the analysis of stream of

finishes all the data is discarded.

Events in general can be classified according to their temporal state. There is previously processed data that

the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

the events happening right now, there is not eno

for a product that provide

need to understand all the technical details that can confuse business users. This was the reason

Oracle Stream Explorer product

that allows the creation of event processing applications through an intuitive

enabled application available to be used both on

pable of providing a zero

The goal of this paper is to provide the basic information necessary to start building applications using

e a solid introduction to the main product features and will show

step how to develop a sample application based on an interesting case study.

Understanding Shapes, Streams, References and Explorations

solid introduction to the most important artifacts created during the

development of event processing applications. It is highly recommended that first time users working with Oracle

Stream Explorer spend some time reading this section before venturing int

, when human beings start learning something new, they mentally break down

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

matched against a structural representation of these objects. In the real world, these so called geons are known as

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

it has a structural representation. Building event processing applic

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

about a data set. It has a name, a list of attributes and their respective data types,

determines from where the data

OEP adapter. Another important aspect about shapes is that they are

stream of events, data is brought into memory but as soon as the analysis

Events in general can be classified according to their temporal state. There is previously processed data that

the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

the events happening right now, there is not enough data to understand the entire context. As mentioned before, the

provides event processing power but without the

need to understand all the technical details that can confuse business users. This was the reason

product; to provide

that allows the creation of event processing applications through an intuitive

enabled application available to be used both on

pable of providing a zero-coding environment for people in

The goal of this paper is to provide the basic information necessary to start building applications using

e a solid introduction to the main product features and will show

step how to develop a sample application based on an interesting case study.

Understanding Shapes, Streams, References and Explorations

solid introduction to the most important artifacts created during the

development of event processing applications. It is highly recommended that first time users working with Oracle

Stream Explorer spend some time reading this section before venturing into the product.

, when human beings start learning something new, they mentally break down

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

bjects. In the real world, these so called geons are known as

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

it has a structural representation. Building event processing applications is no different. Every piece of data being

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

nd their respective data types,

where the data

OEP adapter. Another important aspect about shapes is that they are

events, data is brought into memory but as soon as the analysis

Events in general can be classified according to their temporal state. There is previously processed data that

the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

ugh data to understand the entire context. As mentioned before, the

event processing power but without the

need to understand all the technical details that can confuse business users. This was the reason

to provide business users with a platform

that allows the creation of event processing applications through an intuitive and simple user interface.

enabled application available to be used both on

coding environment for people in

The goal of this paper is to provide the basic information necessary to start building applications using

e a solid introduction to the main product features and will show

step how to develop a sample application based on an interesting case study.

Understanding Shapes, Streams, References and Explorations

solid introduction to the most important artifacts created during the

development of event processing applications. It is highly recommended that first time users working with Oracle

o the product.

, when human beings start learning something new, they mentally break down

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

bjects. In the real world, these so called geons are known as

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

ations is no different. Every piece of data being

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

data set. Without creating a shape, there is no way in Oracle Stream Explorer to process any data.

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

nd their respective data types, and it is

where the data will come from, and behind the scenes

OEP adapter. Another important aspect about shapes is that they are

events, data is brought into memory but as soon as the analysis

Events in general can be classified according to their temporal state. There is previously processed data that

the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

ugh data to understand the entire context. As mentioned before, the

event processing power but without the

need to understand all the technical details that can confuse business users. This was the reason

ss users with a platform

and simple user interface.

enabled application available to be used both on-premise and via

coding environment for people interested in

The goal of this paper is to provide the basic information necessary to start building applications using

e a solid introduction to the main product features and will show

step how to develop a sample application based on an interesting case study.

solid introduction to the most important artifacts created during the

development of event processing applications. It is highly recommended that first time users working with Oracle

, when human beings start learning something new, they mentally break down

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

bjects. In the real world, these so called geons are known as

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

ations is no different. Every piece of data being

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

ny data.

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

it is commonly used when

from, and behind the scenes

OEP adapter. Another important aspect about shapes is that they are

events, data is brought into memory but as soon as the analysis

Events in general can be classified according to their temporal state. There is previously processed data that

the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

ugh data to understand the entire context. As mentioned before, the

event processing power but without the

need to understand all the technical details that can confuse business users. This was the reason

ss users with a platform

and simple user interface.

premise and via

terested in

The goal of this paper is to provide the basic information necessary to start building applications using

e a solid introduction to the main product features and will show

solid introduction to the most important artifacts created during the

development of event processing applications. It is highly recommended that first time users working with Oracle

, when human beings start learning something new, they mentally break down

objects into simple geometric forms called geons. Geons are simple 2D or 3D forms such as cylinders, bricks,

rectangles. When observed together, all geons can describe a higher geon such as a car, but

even when they are observed individually, geons also mean something because in the human brain, every geon is

bjects. In the real world, these so called geons are known as

Shapes are the fundamental building blocks in which all the objects are classified. Every object is a shape therefore

ations is no different. Every piece of data being

analyzed or helping in the analysis is a shape. You need to create a shape to provide a working representation of a

From the technical point of view, a shape in Oracle Stream Explorer is a type of artifact that provides metadata

used when

from, and behind the scenes

OEP adapter. Another important aspect about shapes is that they are

events, data is brought into memory but as soon as the analysis

Events in general can be classified according to their temporal state. There is previously processed data that

the past and there are the ongoing events that represent what is happening right now. It is

undeniable that event processing is all about discovering what is happening right now but, in most cases, with only

ugh data to understand the entire context. As mentioned before, the

Page 5: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

4

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

past events being used to provide more context to the current str

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

may be delaye

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

are the ongoing events and they r

previously processed data, therefore they are facts from the past.

In Oracle Stream Explorer, any

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

analysis requires some of this contextual or historical data to be useful. Any

add more context to

are used to reference already processed data. From the temporal state perspective,

happening right now and the

Explorations and Patterns

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

artifact called an exp

Every exploration needs to be associated w

one stream must exist in the list of sources, and it must be the first

other streams

between

prior to the exploration.

The output r

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

between

4 | INTRODUCTION TO THE

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

past events being used to provide more context to the current str

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

may be delayed. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

are the ongoing events and they r

previously processed data, therefore they are facts from the past.

In Oracle Stream Explorer, any

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

analysis requires some of this contextual or historical data to be useful. Any

add more context to

are used to reference already processed data. From the temporal state perspective,

happening right now and the

Explorations and Patterns

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

artifact called an exp

Every exploration needs to be associated w

one stream must exist in the list of sources, and it must be the first

other streams or references, and there is no limit to the number of

between sources

prior to the exploration.

The output result of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

between sources

INTRODUCTION TO THE ORACLE STREAM EXPLOR

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

past events being used to provide more context to the current str

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

are the ongoing events and they r

previously processed data, therefore they are facts from the past.

In Oracle Stream Explorer, any

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

analysis requires some of this contextual or historical data to be useful. Any

add more context to a stream it is c

are used to reference already processed data. From the temporal state perspective,

happening right now and the references represents

Explorations and Patterns

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

artifact called an exploration.

Every exploration needs to be associated w

one stream must exist in the list of sources, and it must be the first

or references, and there is no limit to the number of

sources and explorations, all

prior to the exploration.

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

sources and exploration

ORACLE STREAM EXPLORER

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

past events being used to provide more context to the current str

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

are the ongoing events and they represent what is happening right now, and the details about each ship represents

previously processed data, therefore they are facts from the past.

In Oracle Stream Explorer, any unbounded sequence

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

analysis requires some of this contextual or historical data to be useful. Any

a stream it is called reference. References are

are used to reference already processed data. From the temporal state perspective,

references represents

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

Every exploration needs to be associated with a list of sources, which

one stream must exist in the list of sources, and it must be the first

or references, and there is no limit to the number of

and explorations, all sources

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

and explorations.

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

past events being used to provide more context to the current str

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

epresent what is happening right now, and the details about each ship represents

previously processed data, therefore they are facts from the past.

unbounded sequence of data used to represent an ongoing event is called strea

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

analysis requires some of this contextual or historical data to be useful. Any

alled reference. References are

are used to reference already processed data. From the temporal state perspective,

references represents facts from the past.

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

ith a list of sources, which

one stream must exist in the list of sources, and it must be the first

or references, and there is no limit to the number of

sources intended to be used in explorations need to be created in advance,

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

past events being used to provide more context to the current stream of events.

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

epresent what is happening right now, and the details about each ship represents

previously processed data, therefore they are facts from the past.

used to represent an ongoing event is called strea

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

analysis requires some of this contextual or historical data to be useful. Any static data

alled reference. References are static and the

are used to reference already processed data. From the temporal state perspective,

facts from the past.

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

ith a list of sources, which can be

one stream must exist in the list of sources, and it must be the first source

or references, and there is no limit to the number of sources used in the list.

intended to be used in explorations need to be created in advance,

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

eam of events.

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

epresent what is happening right now, and the details about each ship represents

used to represent an ongoing event is called strea

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

static data that represents data used to

static and the name came from its usage: they

are used to reference already processed data. From the temporal state perspective, the streams represent

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, pe

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

can be a stream or a reference.

source on the list. The other

used in the list.

intended to be used in explorations need to be created in advance,

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

epresent what is happening right now, and the details about each ship represents

used to represent an ongoing event is called strea

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

that represents data used to

name came from its usage: they

streams represent

During the development of event processing applications, there are a set of common activities that must be

performed to achieve some desired business goal, such as creating junctions, temporal constraints, performing

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

a reference. But, at

on the list. The other sources

Due to the relationship

intended to be used in explorations need to be created in advance,

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

analysis of historical facts still has its value, and in the world of event processing, this value is based on the idea of

For instance, imagine that many ships carrying commercial products continuously send events about their locations

and from those events you need to detect and alert when a particular ship is delayed which means product delivery

d. Assuming that the event holds at least the identifier of the ship, you would still need an external

data source to find out where the ship is coming from, registry, etc. In this context, the events coming from the ships

epresent what is happening right now, and the details about each ship represents

used to represent an ongoing event is called stream. A

stream is continuous; it never stops and provides the raw material for the event processing analysis. But most

that represents data used to

name came from its usage: they

what is

During the development of event processing applications, there are a set of common activities that must be

rforming

aggregation and filtering and customizing the output result. In most cases, these activities must be repeatedly

performed until the desired business goal is reached. In Oracle Stream Explorer, this is accomplished through an

But, at least

sources can be

Due to the relationship

intended to be used in explorations need to be created in advance,

esult of an exploration is invariably a new stream. For this reason, once the exploration is published, it

can be used in the list of sources of other explorations, just like any other stream. Figure 1 shows the relationship

Page 6: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

5

Figure 1

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

logic. For this reason, Oracle Stream Explorer makes available a collection o

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

problem instead of on the implementation details.

The usage of patterns is fairly simple: After choosing

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

following patterns are currently available in Oracle Stream Explorer:

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

export the exploration will be normally determin

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

technical details such as integration, security, performance, scalability and f

As previously me

nothing more than

of an EPN. Because

5 | INTRODUCTION TO THE

Figure 1. The relationship between

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

logic. For this reason, Oracle Stream Explorer makes available a collection o

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

problem instead of on the implementation details.

The usage of patterns is fairly simple: After choosing

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

following patterns are currently available in Oracle Stream Explorer:

» Top N: Used to obtain the first "N" events from the event stream.

» Bottom N

» Up Trend

» Down Trend

» Fluctuation

specific time window.

» Eliminate Duplicates

» Detect Duplicates

» Detect Missing Event

» W: Detects when a given field value rises and falls in "W

» Inverse W

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

export the exploration will be normally determin

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

technical details such as integration, security, performance, scalability and f

As previously me

nothing more than

of an EPN. Because

INTRODUCTION TO THE ORACLE STREAM EXPLOR

. The relationship between

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

logic. For this reason, Oracle Stream Explorer makes available a collection o

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

problem instead of on the implementation details.

The usage of patterns is fairly simple: After choosing

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

following patterns are currently available in Oracle Stream Explorer:

Used to obtain the first "N" events from the event stream.

Bottom N: Used to obtain the last "N" events from the event stream.

Up Trend: Detects when a numeric field shows a specified trend change higher in value.

Down Trend: Detects when a numeric field s

Fluctuation: Detects when a given field value changes in a specific upward or downward fashion within a

specific time window.

Eliminate Duplicates: Eliminates duplicate events in the event stream.

Duplicates: Detects when a given data field has duplicate values within a specified period of time.

Detect Missing Event: Detects when an expected event does not occur within a specific time window.

: Detects when a given field value rises and falls in "W

Inverse W: Use this pattern to detect inverse W.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

export the exploration will be normally determin

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

technical details such as integration, security, performance, scalability and f

As previously mentioned, Oracle Stream Explorer has

nothing more than EPNs (Event Processing Networks)

of an EPN. Because explorations are EPNs, they can be exported

ORACLE STREAM EXPLORER

sources and explorations.

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

logic. For this reason, Oracle Stream Explorer makes available a collection o

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

problem instead of on the implementation details.

The usage of patterns is fairly simple: After choosing

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

following patterns are currently available in Oracle Stream Explorer:

Used to obtain the first "N" events from the event stream.

: Used to obtain the last "N" events from the event stream.

: Detects when a numeric field shows a specified trend change higher in value.

: Detects when a numeric field s

: Detects when a given field value changes in a specific upward or downward fashion within a

: Eliminates duplicate events in the event stream.

: Detects when a given data field has duplicate values within a specified period of time.

: Detects when an expected event does not occur within a specific time window.

: Detects when a given field value rises and falls in "W

: Use this pattern to detect inverse W.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

export the exploration will be normally determin

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

technical details such as integration, security, performance, scalability and f

ntioned, Oracle Stream Explorer has

PNs (Event Processing Networks)

orations are EPNs, they can be exported

and explorations.

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

logic. For this reason, Oracle Stream Explorer makes available a collection o

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

problem instead of on the implementation details.

The usage of patterns is fairly simple: After choosing which pattern needs to be implemented, specific data or key

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

following patterns are currently available in Oracle Stream Explorer:

Used to obtain the first "N" events from the event stream.

: Used to obtain the last "N" events from the event stream.

: Detects when a numeric field shows a specified trend change higher in value.

: Detects when a numeric field shows a specified trend change lower in value.

: Detects when a given field value changes in a specific upward or downward fashion within a

: Eliminates duplicate events in the event stream.

: Detects when a given data field has duplicate values within a specified period of time.

: Detects when an expected event does not occur within a specific time window.

: Detects when a given field value rises and falls in "W

: Use this pattern to detect inverse W.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

export the exploration will be normally determined when the exploration is meant to become an OEP application;

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

technical details such as integration, security, performance, scalability and f

ntioned, Oracle Stream Explorer has OEP as its foundation,

PNs (Event Processing Networks) deployed as

orations are EPNs, they can be exported

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

logic. For this reason, Oracle Stream Explorer makes available a collection of pre

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

which pattern needs to be implemented, specific data or key

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

following patterns are currently available in Oracle Stream Explorer:

Used to obtain the first "N" events from the event stream.

: Used to obtain the last "N" events from the event stream.

: Detects when a numeric field shows a specified trend change higher in value.

hows a specified trend change lower in value.

: Detects when a given field value changes in a specific upward or downward fashion within a

: Eliminates duplicate events in the event stream.

: Detects when a given data field has duplicate values within a specified period of time.

: Detects when an expected event does not occur within a specific time window.

: Detects when a given field value rises and falls in "W" fashion over a specified time window.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

ed when the exploration is meant to become an OEP application;

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

technical details such as integration, security, performance, scalability and fault

as its foundation,

deployed as OEP applications. Figure 2 show

orations are EPNs, they can be exported from Oracle Stream Explorer

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

f pre-built explorations called patterns.

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

which pattern needs to be implemented, specific data or key

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

: Detects when a numeric field shows a specified trend change higher in value.

hows a specified trend change lower in value.

: Detects when a given field value changes in a specific upward or downward fashion within a

: Eliminates duplicate events in the event stream.

: Detects when a given data field has duplicate values within a specified period of time.

: Detects when an expected event does not occur within a specific time window.

" fashion over a specified time window.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

ed when the exploration is meant to become an OEP application;

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

ault-tolerance.

as its foundation, and the generated explorations are

OEP applications. Figure 2 show

Oracle Stream Explorer

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

built explorations called patterns.

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

which pattern needs to be implemented, specific data or key

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

: Detects when a numeric field shows a specified trend change higher in value.

hows a specified trend change lower in value.

: Detects when a given field value changes in a specific upward or downward fashion within a

: Detects when a given data field has duplicate values within a specified period of time.

: Detects when an expected event does not occur within a specific time window.

" fashion over a specified time window.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

ed when the exploration is meant to become an OEP application;

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

erated explorations are

OEP applications. Figure 2 shows an example

Oracle Stream Explorer and imported again

Working with explorations can be very time consuming, especially if the scenario being explored requires complex

built explorations called patterns.

Patterns are generic solutions to recurring common problems, and they help to keep the focus on the business

which pattern needs to be implemented, specific data or key

fields will be required, and after providing all of them the entire exploration will be automatically generated. The

: Detects when a given field value changes in a specific upward or downward fashion within a

: Detects when a given data field has duplicate values within a specified period of time.

: Detects when an expected event does not occur within a specific time window.

Once an exploration is built it can be used as a source for another exploration or it can be exported. The decision to

ed when the exploration is meant to become an OEP application;

originally created in Oracle Stream Explorer to reach the business goal but to be later enhanced in OEP to handle

erated explorations are

s an example

and imported again

Page 7: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

6

into a development environment

JAR file is generated with

CQL (Continuous Query Language) statements.

Figure 2

Case Study: Implementing the Minority Report Mall Scene

In July of 2002, 2

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

The film revolves around the story of a spe

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

faces a challenge since he is n

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

of its own eyes and have then surgically exchanged wit

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised

saying "Hello Mr. Yakamoto, welcome back to the GAP".

What this scene taught us is that the technology of sensors combined with event processing can pr

individualized experience for

customer's

retinas and send the results to a capable event processing technology for processing. The event processing

c

after recogniz

This scenario would not be so challenging if it was not for the fact that

custom message while the person is near the store. If the current business intelligence techniques were used in this

scenario, the events

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

6 | INTRODUCTION TO THE

into a development environment

JAR file is generated with

CQL (Continuous Query Language) statements.

Figure 2. Example of an EPN generated from the implementation of an exploration.

Case Study: Implementing the Minority Report Mall Scene

In July of 2002, 2

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

The film revolves around the story of a spe

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

faces a challenge since he is n

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

of its own eyes and have then surgically exchanged wit

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised

saying "Hello Mr. Yakamoto, welcome back to the GAP".

What this scene taught us is that the technology of sensors combined with event processing can pr

individualized experience for

customer's personal preferences

retinas and send the results to a capable event processing technology for processing. The event processing

comes into play when it has to,

after recognizing

This scenario would not be so challenging if it was not for the fact that

custom message while the person is near the store. If the current business intelligence techniques were used in this

scenario, the events

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

INTRODUCTION TO THE ORACLE STREAM EXPLOR

into a development environment

JAR file is generated with common EPN artifacts such as e

CQL (Continuous Query Language) statements.

Example of an EPN generated from the implementation of an exploration.

Case Study: Implementing the Minority Report Mall Scene

In July of 2002, 20th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

The film revolves around the story of a spe

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

faces a challenge since he is n

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

of its own eyes and have then surgically exchanged wit

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised

saying "Hello Mr. Yakamoto, welcome back to the GAP".

What this scene taught us is that the technology of sensors combined with event processing can pr

individualized experience for customers,

personal preferences

retinas and send the results to a capable event processing technology for processing. The event processing

omes into play when it has to,

ing who the person is, greeting them with a custom message.

This scenario would not be so challenging if it was not for the fact that

custom message while the person is near the store. If the current business intelligence techniques were used in this

scenario, the events from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

ORACLE STREAM EXPLORER

into a development environment such as Oracle Fusion Middleware JDeveloper. When the exploration is ex

common EPN artifacts such as e

CQL (Continuous Query Language) statements.

Example of an EPN generated from the implementation of an exploration.

Case Study: Implementing the Minority Report Mall Scene

0th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

The film revolves around the story of a special police program called PreCrime. Using three mutated humans stored

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

faces a challenge since he is now the main suspect of a future crime. In the year of 2050, nearly all public places

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

of its own eyes and have then surgically exchanged wit

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised

saying "Hello Mr. Yakamoto, welcome back to the GAP".

What this scene taught us is that the technology of sensors combined with event processing can pr

customers, reacting appropriately when ne

personal preferences. The sensor in this

retinas and send the results to a capable event processing technology for processing. The event processing

omes into play when it has to, from a stream of multiple

who the person is, greeting them with a custom message.

This scenario would not be so challenging if it was not for the fact that

custom message while the person is near the store. If the current business intelligence techniques were used in this

from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

Oracle Fusion Middleware JDeveloper. When the exploration is ex

common EPN artifacts such as event types, adapters, caches, channels, processors and

CQL (Continuous Query Language) statements.

Example of an EPN generated from the implementation of an exploration.

Case Study: Implementing the Minority Report Mall Scene

0th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

cial police program called PreCrime. Using three mutated humans stored

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

ow the main suspect of a future crime. In the year of 2050, nearly all public places

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

of its own eyes and have then surgically exchanged with a pair of eyes that belonged to a deceased person known

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised

saying "Hello Mr. Yakamoto, welcome back to the GAP".

What this scene taught us is that the technology of sensors combined with event processing can pr

reacting appropriately when ne

sensor in this context are the eye scanners that continuously read people's

retinas and send the results to a capable event processing technology for processing. The event processing

m a stream of multiple events;

who the person is, greeting them with a custom message.

This scenario would not be so challenging if it was not for the fact that

custom message while the person is near the store. If the current business intelligence techniques were used in this

from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

Oracle Fusion Middleware JDeveloper. When the exploration is ex

vent types, adapters, caches, channels, processors and

Example of an EPN generated from the implementation of an exploration.

Case Study: Implementing the Minority Report Mall Scene

0th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

cial police program called PreCrime. Using three mutated humans stored

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

ow the main suspect of a future crime. In the year of 2050, nearly all public places

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

h a pair of eyes that belonged to a deceased person known

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised

What this scene taught us is that the technology of sensors combined with event processing can pr

reacting appropriately when needed and

are the eye scanners that continuously read people's

retinas and send the results to a capable event processing technology for processing. The event processing

events; detect which one of them is near the store and,

who the person is, greeting them with a custom message.

This scenario would not be so challenging if it was not for the fact that the event processing system

custom message while the person is near the store. If the current business intelligence techniques were used in this

from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

Oracle Fusion Middleware JDeveloper. When the exploration is ex

vent types, adapters, caches, channels, processors and

Case Study: Implementing the Minority Report Mall Scene

0th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

cial police program called PreCrime. Using three mutated humans stored

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

murders before they happen, deploying the police force in preemptive fashion to arrest the future murderers. In the

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesti

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

ow the main suspect of a future crime. In the year of 2050, nearly all public places

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

h a pair of eyes that belonged to a deceased person known

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

situation when, looking for some new clothes, he enters in a GAP store and it is surprised with a greeting message

What this scene taught us is that the technology of sensors combined with event processing can pr

eded and with information about the

are the eye scanners that continuously read people's

retinas and send the results to a capable event processing technology for processing. The event processing

detect which one of them is near the store and,

the event processing system

custom message while the person is near the store. If the current business intelligence techniques were used in this

from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

Oracle Fusion Middleware JDeveloper. When the exploration is exported, a

vent types, adapters, caches, channels, processors and

0th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

cial police program called PreCrime. Using three mutated humans stored

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

arrest the future murderers. In the

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

John Anderton; a police captain responsible for the PreCrime program. Besides presenting a very interesting story,

the film also has lots of scenes in which high technology is used, revealing how the near future could be.

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

ow the main suspect of a future crime. In the year of 2050, nearly all public places

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

h a pair of eyes that belonged to a deceased person known

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

with a greeting message

What this scene taught us is that the technology of sensors combined with event processing can provide

with information about the

are the eye scanners that continuously read people's

retinas and send the results to a capable event processing technology for processing. The event processing

detect which one of them is near the store and,

the event processing system must trigger the

custom message while the person is near the store. If the current business intelligence techniques were used in this

from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

ported, a

vent types, adapters, caches, channels, processors and

0th Century Fox's studios released Minority Report. Directed by Steven Spielberg and starring Tom

Cruise as the main character, the film rapidly became one of the most successful science fiction movies ever made.

cial police program called PreCrime. Using three mutated humans stored

in a special chamber where they experience precognitive visions, the PreCrime program is designed to stop

arrest the future murderers. In the

film, the PreCrime program is established in Washington D.C in the year of 2050, and Tom Cruise plays the role of

ng story,

In one of those scenes, John Anderton needs to get in into the chamber where the mutated humans are, but he

ow the main suspect of a future crime. In the year of 2050, nearly all public places

have eye scanners that can identify every citizen. So in order to not be apprehended he has no choice but to get rid

h a pair of eyes that belonged to a deceased person known

as Yakamoto. After the surgery and already in possession of Mr. Yakamoto eyes, he finds himself into an odd

with a greeting message

ovide an

with information about the

are the eye scanners that continuously read people's

retinas and send the results to a capable event processing technology for processing. The event processing part

detect which one of them is near the store and,

must trigger the

custom message while the person is near the store. If the current business intelligence techniques were used in this

from the scanned people would be stored in a staging database where a scheduled job would

load the last "N" scans that were near the store, possibly an hour, day or even a week later. To make sense, the

Page 8: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

7

custom message must be delivered while the person is

a few seconds.

This is when the technology of event processing comes into play making it feasible to actually build a system that

could make this type of science fiction scene possible. Even

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be develop

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

near the stor

order to make the implementation feasible, the following assumptions will be assumed:

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

Part One: Creating the Solution Design for the Scenario

When designing a solution that is going to be implemented in Oracle St

artifacts that wi

all data that is going to flow through the event processing application, the first step of the

of a conceptual model, separating the shapes in

are going to be needed. Figure 3 shows the conceptual model of this case study.

Figure 3

In order to classify each shape according to its

associated with

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from the Oracle Stream Explorer perspective, they

invariably a new stream.

instead of

7 | INTRODUCTION TO THE

custom message must be delivered while the person is

a few seconds.

This is when the technology of event processing comes into play making it feasible to actually build a system that

could make this type of science fiction scene possible. Even

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be develop

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

near the store to the moment in which he or she

order to make the implementation feasible, the following assumptions will be assumed:

» Each eye scan event holds the person retina and his or her location.

» All customer information is available through a database table.

» The custom greetings are avai

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

Part One: Creating the Solution Design for the Scenario

When designing a solution that is going to be implemented in Oracle St

artifacts that will be required

all data that is going to flow through the event processing application, the first step of the

of a conceptual model, separating the shapes in

are going to be needed. Figure 3 shows the conceptual model of this case study.

Figure 3. Shapes in the

In order to classify each shape according to its

associated with ongoing events are stereotyped as streams and shapes

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from the Oracle Stream Explorer perspective, they

invariably a new stream.

instead of a stream, because in the final analysis every exploration is a stream.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

custom message must be delivered while the person is

This is when the technology of event processing comes into play making it feasible to actually build a system that

could make this type of science fiction scene possible. Even

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be develop

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

e to the moment in which he or she

order to make the implementation feasible, the following assumptions will be assumed:

Each eye scan event holds the person retina and his or her location.

All customer information is available through a database table.

The custom greetings are avai

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

Part One: Creating the Solution Design for the Scenario

When designing a solution that is going to be implemented in Oracle St

ll be required, starting with the necessary

all data that is going to flow through the event processing application, the first step of the

of a conceptual model, separating the shapes in

are going to be needed. Figure 3 shows the conceptual model of this case study.

Shapes in the conceptual model of the case study.

In order to classify each shape according to its

ongoing events are stereotyped as streams and shapes

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from the Oracle Stream Explorer perspective, they

invariably a new stream. For this reason, it would not be considered a mistake using the stereotype exploration

stream, because in the final analysis every exploration is a stream.

ORACLE STREAM EXPLORER

custom message must be delivered while the person is

This is when the technology of event processing comes into play making it feasible to actually build a system that

could make this type of science fiction scene possible. Even

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be develop

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

e to the moment in which he or she

order to make the implementation feasible, the following assumptions will be assumed:

Each eye scan event holds the person retina and his or her location.

All customer information is available through a database table.

The custom greetings are available through a database table.

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

Part One: Creating the Solution Design for the Scenario

When designing a solution that is going to be implemented in Oracle St

, starting with the necessary

all data that is going to flow through the event processing application, the first step of the

of a conceptual model, separating the shapes in

are going to be needed. Figure 3 shows the conceptual model of this case study.

conceptual model of the case study.

In order to classify each shape according to its

ongoing events are stereotyped as streams and shapes

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from the Oracle Stream Explorer perspective, they

r this reason, it would not be considered a mistake using the stereotype exploration

stream, because in the final analysis every exploration is a stream.

custom message must be delivered while the person is in close proximity the store, a situation that may only last for

This is when the technology of event processing comes into play making it feasible to actually build a system that

could make this type of science fiction scene possible. Event processing enables the analysis of what is happening

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be develop

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

e to the moment in which he or she is recognized to finally, receive a custom greeting mess

order to make the implementation feasible, the following assumptions will be assumed:

Each eye scan event holds the person retina and his or her location.

All customer information is available through a database table.

lable through a database table.

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

Part One: Creating the Solution Design for the Scenario

When designing a solution that is going to be implemented in Oracle St

, starting with the necessary shapes. Considering that

all data that is going to flow through the event processing application, the first step of the

of a conceptual model, separating the shapes into two categories: the shapes already available and the shapes that

are going to be needed. Figure 3 shows the conceptual model of this case study.

conceptual model of the case study.

In order to classify each shape according to its usage, the conceptual model

ongoing events are stereotyped as streams and shapes

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from the Oracle Stream Explorer perspective, they derive from explorations and the output result of an explorat

r this reason, it would not be considered a mistake using the stereotype exploration

stream, because in the final analysis every exploration is a stream.

in close proximity the store, a situation that may only last for

This is when the technology of event processing comes into play making it feasible to actually build a system that

t processing enables the analysis of what is happening

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be develop

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

is recognized to finally, receive a custom greeting mess

order to make the implementation feasible, the following assumptions will be assumed:

Each eye scan event holds the person retina and his or her location.

All customer information is available through a database table.

lable through a database table.

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

When designing a solution that is going to be implemented in Oracle Stream Explorer, you need to detail all the

shapes. Considering that

all data that is going to flow through the event processing application, the first step of the

two categories: the shapes already available and the shapes that

are going to be needed. Figure 3 shows the conceptual model of this case study.

the conceptual model

ongoing events are stereotyped as streams and shapes associated with

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from explorations and the output result of an explorat

r this reason, it would not be considered a mistake using the stereotype exploration

stream, because in the final analysis every exploration is a stream.

in close proximity the store, a situation that may only last for

This is when the technology of event processing comes into play making it feasible to actually build a system that

t processing enables the analysis of what is happening

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

scene from the Minority Report film will be the case study that is going to be developed in this section. Using Oracle

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

is recognized to finally, receive a custom greeting mess

order to make the implementation feasible, the following assumptions will be assumed:

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

ream Explorer, you need to detail all the

shapes. Considering that shapes are the representation of

all data that is going to flow through the event processing application, the first step of the solution design is t

two categories: the shapes already available and the shapes that

are going to be needed. Figure 3 shows the conceptual model of this case study.

the conceptual model may include stereotypes. Shapes

associated with already processed data are

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from explorations and the output result of an explorat

r this reason, it would not be considered a mistake using the stereotype exploration

stream, because in the final analysis every exploration is a stream.

in close proximity the store, a situation that may only last for

This is when the technology of event processing comes into play making it feasible to actually build a system that

t processing enables the analysis of what is happening

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

ed in this section. Using Oracle

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

is recognized to finally, receive a custom greeting mess

The development of this case study will be divided in two parts. The first part will cover all the details related to the

solution design, where all the necessary artifacts will be identified. The second part will cover the development of

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

ream Explorer, you need to detail all the

shapes are the representation of

solution design is t

two categories: the shapes already available and the shapes that

include stereotypes. Shapes

already processed data are

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from explorations and the output result of an explorat

r this reason, it would not be considered a mistake using the stereotype exploration

in close proximity the store, a situation that may only last for

This is when the technology of event processing comes into play making it feasible to actually build a system that

t processing enables the analysis of what is happening

right now, handling events while they are still ongoing and detecting complex relationships between them. The mall

ed in this section. Using Oracle

Stream Explorer, this case study is going to be built through explorations, covering the detection of when a person is

is recognized to finally, receive a custom greeting message. In

The development of this case study will be divided in two parts. The first part will cover all the details related to the

development of

those artifacts in Oracle Stream Explorer, showing how to create them and how to handle the configuration details.

ream Explorer, you need to detail all the

shapes are the representation of

solution design is to build

two categories: the shapes already available and the shapes that

include stereotypes. Shapes

already processed data are

stereotyped as references. All the shapes found in the needed category may be stereotyped as streams because,

from explorations and the output result of an exploration is

r this reason, it would not be considered a mistake using the stereotype exploration

Page 9: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

8

The next step is the inclusion of the causality relationships between all the shapes, emp

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

the causality rel

Figure 4

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

in advance how many explorations will need to be

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a be

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

a reasonable level.

Each exploration must have a meaningful name. As a rule of thumb, consider that each expl

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ex

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eye scan stream ne

find out who the person near the store is.

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

Explorer provides a high l

any,

8 | INTRODUCTION TO THE

The next step is the inclusion of the causality relationships between all the shapes, emp

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

the causality relationships.

Figure 4. Conceptual model refined to include the causality relationships.

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

in advance how many explorations will need to be

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a be

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

a reasonable level.

Each exploration must have a meaningful name. As a rule of thumb, consider that each expl

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

exploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eye scan stream ne

find out who the person near the store is.

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

Explorer provides a high l

any, software development

INTRODUCTION TO THE ORACLE STREAM EXPLOR

The next step is the inclusion of the causality relationships between all the shapes, emp

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

ationships.

Conceptual model refined to include the causality relationships.

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

in advance how many explorations will need to be

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a be

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

a reasonable level.

Each exploration must have a meaningful name. As a rule of thumb, consider that each expl

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eye scan stream needs to be correlated with the scanEntry attribute present in the customer reference, in order to

find out who the person near the store is.

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

Explorer provides a high level of abstraction in regards to implementation details. For this reason, people with few

software development skills can use the product directly, being responsible for the end

ORACLE STREAM EXPLORER

The next step is the inclusion of the causality relationships between all the shapes, emp

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

Conceptual model refined to include the causality relationships.

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

in advance how many explorations will need to be

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a be

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

Each exploration must have a meaningful name. As a rule of thumb, consider that each expl

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eds to be correlated with the scanEntry attribute present in the customer reference, in order to

find out who the person near the store is.

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

evel of abstraction in regards to implementation details. For this reason, people with few

skills can use the product directly, being responsible for the end

The next step is the inclusion of the causality relationships between all the shapes, emp

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

Conceptual model refined to include the causality relationships.

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

in advance how many explorations will need to be built is important because it can reveal how much development

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a be

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

Each exploration must have a meaningful name. As a rule of thumb, consider that each expl

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eds to be correlated with the scanEntry attribute present in the customer reference, in order to

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

evel of abstraction in regards to implementation details. For this reason, people with few

skills can use the product directly, being responsible for the end

The next step is the inclusion of the causality relationships between all the shapes, emp

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

Conceptual model refined to include the causality relationships.

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

built is important because it can reveal how much development

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a be

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

Each exploration must have a meaningful name. As a rule of thumb, consider that each expl

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eds to be correlated with the scanEntry attribute present in the customer reference, in order to

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

evel of abstraction in regards to implementation details. For this reason, people with few

skills can use the product directly, being responsible for the end

The next step is the inclusion of the causality relationships between all the shapes, emphasizing how the processing

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

built is important because it can reveal how much development

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

causalities need to be implemented in different explorations, it is considered a best practice to break down the

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

Each exploration must have a meaningful name. As a rule of thumb, consider that each exploration must be named

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eds to be correlated with the scanEntry attribute present in the customer reference, in order to

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

evel of abstraction in regards to implementation details. For this reason, people with few

skills can use the product directly, being responsible for the end-to

hasizing how the processing

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the executio

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

built is important because it can reveal how much development

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

st practice to break down the

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

oration must be named

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eds to be correlated with the scanEntry attribute present in the customer reference, in order to

The target audience of this description will be the person responsible for the implementation of the artifacts in Or

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

evel of abstraction in regards to implementation details. For this reason, people with few

to-end implementation.

hasizing how the processing

of one or more shapes will result in the creation of a new shape. In this context, the term processing refers to the

usage of any activity of junction, temporal constraint, aggregation or filtering, performed during the execution of an

exploration. It is also important that during this step the attributes of each shape be included, just like the mapping

between the attributes of the shapes available and the shapes needed. Figure 4 shows the conceptual model with

Each lane shown on figure 4 represents an exploration that needs to be built in Oracle Stream Explorer. Finding out

built is important because it can reveal how much development

effort the scenario will demand from the implementation perspective. While there is no rule that dictates that

st practice to break down the

business problem into smaller explorations, promoting better reuse of the artifacts and moving the complexity toward

oration must be named

with the shape name that it intends to create. If business rules need to be applied during the execution of the

exploration, it is considered a best practice to provide a description of these business rules. For instance, the first

ploration has as business rule the utilization of the latitude and longitude attributes present in the eye scan stream

to detect if the customer is near the store or not. Also considered as a business rule, the eyeScan attribute from the

eds to be correlated with the scanEntry attribute present in the customer reference, in order to

The target audience of this description will be the person responsible for the implementation of the artifacts in Oracle

Stream Explorer, which can be the same person that builds the conceptual model or it can be someone different. In

most cases, the same person that builds the conceptual model also implements the artifacts since Oracle Stream

evel of abstraction in regards to implementation details. For this reason, people with few, if

end implementation.

Page 10: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

9

If different people need to cooperate to impl

help in the communication throughout the entire project implementation. This is particularly important

solution designer and the implementer belong to different companies or,

Figure 5 shows the final version of the conceptual model.

Figure 5

The conceptual model detailed in figure 5 provides a solid foundat

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

commonly found in this type of solution.

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Before start

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

of

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementati

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

be found in the Appendix A.

Considering that two

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

9 | INTRODUCTION TO THE

f different people need to cooperate to impl

help in the communication throughout the entire project implementation. This is particularly important

solution designer and the implementer belong to different companies or,

Figure 5 shows the final version of the conceptual model.

Figure 5. Final version of the conceptual model with the description of the business rules.

The conceptual model detailed in figure 5 provides a solid foundat

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

commonly found in this type of solution.

Part Two: Implementing the Artifacts in Oracle Stream Explorer

Before starting the imp

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

of-the-box supported databases are Oracle, SQL

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementati

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

be found in the Appendix A.

Considering that two

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

INTRODUCTION TO THE ORACLE STREAM EXPLOR

f different people need to cooperate to impl

help in the communication throughout the entire project implementation. This is particularly important

solution designer and the implementer belong to different companies or,

Figure 5 shows the final version of the conceptual model.

Final version of the conceptual model with the description of the business rules.

The conceptual model detailed in figure 5 provides a solid foundat

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

commonly found in this type of solution.

Part Two: Implementing the Artifacts in Oracle Stream Explorer

the implementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

box supported databases are Oracle, SQL

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementati

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

be found in the Appendix A.

Considering that two sources that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

ORACLE STREAM EXPLORER

f different people need to cooperate to implement the scenario, providing a description of the business rules can

help in the communication throughout the entire project implementation. This is particularly important

solution designer and the implementer belong to different companies or,

Figure 5 shows the final version of the conceptual model.

Final version of the conceptual model with the description of the business rules.

The conceptual model detailed in figure 5 provides a solid foundat

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

commonly found in this type of solution.

Part Two: Implementing the Artifacts in Oracle Stream Explorer

lementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

box supported databases are Oracle, SQL

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementati

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

ement the scenario, providing a description of the business rules can

help in the communication throughout the entire project implementation. This is particularly important

solution designer and the implementer belong to different companies or,

Figure 5 shows the final version of the conceptual model.

Final version of the conceptual model with the description of the business rules.

The conceptual model detailed in figure 5 provides a solid foundat

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

Part Two: Implementing the Artifacts in Oracle Stream Explorer

lementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

box supported databases are Oracle, SQL Server 2005 and Derby. Other JDBC compliant databases can be

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementati

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

ement the scenario, providing a description of the business rules can

help in the communication throughout the entire project implementation. This is particularly important

solution designer and the implementer belong to different companies or, when

Final version of the conceptual model with the description of the business rules.

The conceptual model detailed in figure 5 provides a solid foundation for the implementation of the case study. In

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

Part Two: Implementing the Artifacts in Oracle Stream Explorer

lementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

Server 2005 and Derby. Other JDBC compliant databases can be

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementati

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

ement the scenario, providing a description of the business rules can

help in the communication throughout the entire project implementation. This is particularly important

en they are geographically separated.

Final version of the conceptual model with the description of the business rules.

ion for the implementation of the case study. In

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution des

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

lementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

Server 2005 and Derby. Other JDBC compliant databases can be

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

create and populate the two database tables that will be used during the implementation. This SQL script can be

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

ement the scenario, providing a description of the business rules can

help in the communication throughout the entire project implementation. This is particularly important when

they are geographically separated.

ion for the implementation of the case study. In

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

business scenarios. The steps shown in this section are not intended to be used as a solution design methodology.

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

lementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out

Server 2005 and Derby. Other JDBC compliant databases can be

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

on. This SQL script can be

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can

that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection p

ement the scenario, providing a description of the business rules can

when the

they are geographically separated.

ion for the implementation of the case study. In

most cases, the creation of the conceptual model will be elaborated mentally during the implementation of the

ign methodology.

Instead, they show how a typical event processing application should be designed and what kinds of concerns are

lementation of the artifacts in Oracle Stream Explorer, it is necessary to make sure that some

prerequisites are satisfied. First, a running database with DDL and DML permissions needs to be available. The out-

Server 2005 and Derby. Other JDBC compliant databases can be

used, but deployment of their JDBC drivers may be necessary. Secondly, an SQL script needs to be executed to

on. This SQL script can be

found in the Appendix A of this paper. Finally, a CSV file will be used during the implementation of the eye scan

stream artifact. Sample data from the content of this file, just like the URL to download its complete version can also

that act as references to a database table will need to be created in Oracle Stream

Explorer, a database connection pool needs to be set up. In order to be able to set up a database connection pool,

Page 11: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

10

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

up and running, open a web browser and access the following URL:

address or hostname where the server is running and port is the HTTP

Figure 6 shows the login screen of the Oracle Event Processing Visualizer.

Figure 6

Appropriate

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

where Oracle St

tabs. To start the creation of the databas

10 | INTRODUCTION TO THE

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

up and running, open a web browser and access the following URL:

address or hostname where the server is running and port is the HTTP

Figure 6 shows the login screen of the Oracle Event Processing Visualizer.

Figure 6. Oracle Event Processing Visualizer lo

Appropriate credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

where Oracle St

tabs. To start the creation of the databas

INTRODUCTION TO THE ORACLE STREAM EXPLOR

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

up and running, open a web browser and access the following URL:

address or hostname where the server is running and port is the HTTP

Figure 6 shows the login screen of the Oracle Event Processing Visualizer.

. Oracle Event Processing Visualizer lo

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

where Oracle Stream Explorer is running. This will open the window where all the server details will be available via

tabs. To start the creation of the databas

ORACLE STREAM EXPLORER

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

up and running, open a web browser and access the following URL:

address or hostname where the server is running and port is the HTTP

Figure 6 shows the login screen of the Oracle Event Processing Visualizer.

. Oracle Event Processing Visualizer login screen.

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

ream Explorer is running. This will open the window where all the server details will be available via

tabs. To start the creation of the database connection pool, click in the

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

up and running, open a web browser and access the following URL:

address or hostname where the server is running and port is the HTTP

Figure 6 shows the login screen of the Oracle Event Processing Visualizer.

gin screen.

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

ream Explorer is running. This will open the window where all the server details will be available via

e connection pool, click in the

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

up and running, open a web browser and access the following URL: http://host:port/wlevs

address or hostname where the server is running and port is the HTTP-enabled port configured for external access.

Figure 6 shows the login screen of the Oracle Event Processing Visualizer.

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

ream Explorer is running. This will open the window where all the server details will be available via

e connection pool, click in the DataSource

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

http://host:port/wlevs. In this UR

enabled port configured for external access.

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

ream Explorer is running. This will open the window where all the server details will be available via

DataSource tab as shown in figure 7.

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

. In this URL, host is the IP

enabled port configured for external access.

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

ream Explorer is running. This will open the window where all the server details will be available via

as shown in figure 7.

administrative access to the Oracle Event Processing Visualizer will be necessary. With the Oracle Stream Explorer

L, host is the IP

enabled port configured for external access.

credentials will be needed to access this console. If you do not have these credentials, ask your Oracle

Stream Explorer administrator to create them for you. Once logged in to the console, access the server instance

ream Explorer is running. This will open the window where all the server details will be available via

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11

Figure 7

The

button to create a new database connection pool. The new data source creation wizard will then start. Ente

value "jdbc/minorityReport" for both the

Figure 8

Set the

here is only reading the data available in the database table, and setting this field to none decreases the overhead

incurred in the database. Next, click on the

the database.

11 | INTRODUCTION TO THE

Figure 7. Starting the database

The DataSource

button to create a new database connection pool. The new data source creation wizard will then start. Ente

value "jdbc/minorityReport" for both the

Figure 8. Starting the creation of the database connection pool.

Set the Global Transaction Protocol

here is only reading the data available in the database table, and setting this field to none decreases the overhead

incurred in the database. Next, click on the

the database.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Starting the database connection pool creation in the console.

DataSource tab shows all the da

button to create a new database connection pool. The new data source creation wizard will then start. Ente

value "jdbc/minorityReport" for both the

Starting the creation of the database connection pool.

Global Transaction Protocol

here is only reading the data available in the database table, and setting this field to none decreases the overhead

incurred in the database. Next, click on the

ORACLE STREAM EXPLORER

connection pool creation in the console.

tab shows all the database connection pools already

button to create a new database connection pool. The new data source creation wizard will then start. Ente

value "jdbc/minorityReport" for both the Name and the

Starting the creation of the database connection pool.

Global Transaction Protocol field to "None" as shown in figure 8. This is very important because the intention

here is only reading the data available in the database table, and setting this field to none decreases the overhead

incurred in the database. Next, click on the Global Tra

connection pool creation in the console.

tabase connection pools already

button to create a new database connection pool. The new data source creation wizard will then start. Ente

and the JNDI Name

Starting the creation of the database connection pool.

field to "None" as shown in figure 8. This is very important because the intention

here is only reading the data available in the database table, and setting this field to none decreases the overhead

Global Transaction Protocol

connection pool creation in the console.

tabase connection pools already created for that server. Click on the

button to create a new database connection pool. The new data source creation wizard will then start. Ente

JNDI Name fields, as shown in figure 8.

field to "None" as shown in figure 8. This is very important because the intention

here is only reading the data available in the database table, and setting this field to none decreases the overhead

nsaction Protocol tab to enter the JDBC connection details of

created for that server. Click on the

button to create a new database connection pool. The new data source creation wizard will then start. Ente

fields, as shown in figure 8.

field to "None" as shown in figure 8. This is very important because the intention

here is only reading the data available in the database table, and setting this field to none decreases the overhead

tab to enter the JDBC connection details of

created for that server. Click on the

button to create a new database connection pool. The new data source creation wizard will then start. Enter with the

fields, as shown in figure 8.

field to "None" as shown in figure 8. This is very important because the intention

here is only reading the data available in the database table, and setting this field to none decreases the overhead

tab to enter the JDBC connection details of

created for that server. Click on the Add

r with the

field to "None" as shown in figure 8. This is very important because the intention

here is only reading the data available in the database table, and setting this field to none decreases the overhead

tab to enter the JDBC connection details of

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12

Figure 9

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the

the configuration of the JDBC con

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artif

Stream Explorer application using the following URL:

access the Oracle Event Processing Visualizer, and the same credentials t

Figure 10 shows the login screen of the Oracle Stream Explorer.

Figure 10

12 | INTRODUCTION TO THE

Figure 9. Entering the JDBC connection details of the database.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the

the configuration of the JDBC con

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artif

Stream Explorer application using the following URL:

access the Oracle Event Processing Visualizer, and the same credentials t

Figure 10 shows the login screen of the Oracle Stream Explorer.

Figure 10. Login screen of the Oracle Stream Explorer.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Entering the JDBC connection details of the database.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the

the configuration of the JDBC con

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artif

Stream Explorer application using the following URL:

access the Oracle Event Processing Visualizer, and the same credentials t

Figure 10 shows the login screen of the Oracle Stream Explorer.

Login screen of the Oracle Stream Explorer.

ORACLE STREAM EXPLORER

Entering the JDBC connection details of the database.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the

the configuration of the JDBC connection details of your database is finished, click in the

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artif

Stream Explorer application using the following URL:

access the Oracle Event Processing Visualizer, and the same credentials t

Figure 10 shows the login screen of the Oracle Stream Explorer.

Login screen of the Oracle Stream Explorer.

Entering the JDBC connection details of the database.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the

nection details of your database is finished, click in the

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artif

Stream Explorer application using the following URL: http://host:port/sx

access the Oracle Event Processing Visualizer, and the same credentials t

Figure 10 shows the login screen of the Oracle Stream Explorer.

Login screen of the Oracle Stream Explorer.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the

nection details of your database is finished, click in the

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artif

http://host:port/sx. Those are the same host and port used to

access the Oracle Event Processing Visualizer, and the same credentials that were used before should work here.

Figure 10 shows the login screen of the Oracle Stream Explorer.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

implementation. Regardless of which type of database is being used, set the value of the Use XA

nection details of your database is finished, click in the Save

creation of the database connection pool. Figure 9 shows an example of this configuration.

With the database connection pool properly created, the development of the artifacts can start. Access the Oracle

. Those are the same host and port used to

hat were used before should work here.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

Use XA field to false. Once

Save button to finish the

acts can start. Access the Oracle

. Those are the same host and port used to

hat were used before should work here.

Enter the requested JDBC connections details of the database that contains the two tables needed for this

field to false. Once

button to finish the

acts can start. Access the Oracle

. Those are the same host and port used to

hat were used before should work here.

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13

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

of the business do

without setting a business domain tag,

The catalog screen is where artifacts in Oracl

previously created in the

explorations, streams and references. To create artifacts, you mu

New Item

Figure 11

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

field enter the v

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

13 | INTRODUCTION TO THE

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

of the business do

without setting a business domain tag,

The catalog screen is where artifacts in Oracl

previously created in the

explorations, streams and references. To create artifacts, you mu

New Item combo box, just as shown in figure 11.

Figure 11. Requesting the creation of new artifacts in Oracle Stream Explorer.

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

field enter the value "CustomerData". Provide the tag "customer" in the

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

of the business domain and also drives you directly to the catalog screen. The catalog screen can also be accessed

without setting a business domain tag,

The catalog screen is where artifacts in Oracl

previously created in the main

explorations, streams and references. To create artifacts, you mu

combo box, just as shown in figure 11.

Requesting the creation of new artifacts in Oracle Stream Explorer.

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

alue "CustomerData". Provide the tag "customer" in the

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

ORACLE STREAM EXPLORER

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

main and also drives you directly to the catalog screen. The catalog screen can also be accessed

without setting a business domain tag, via the Catalog

The catalog screen is where artifacts in Oracle Stream Explorer are created and maintained. It displays all artifacts

main table on the center of the screen,

explorations, streams and references. To create artifacts, you mu

combo box, just as shown in figure 11.

Requesting the creation of new artifacts in Oracle Stream Explorer.

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

alue "CustomerData". Provide the tag "customer" in the

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

main and also drives you directly to the catalog screen. The catalog screen can also be accessed

Catalog button, found on the top right corner of the home screen.

e Stream Explorer are created and maintained. It displays all artifacts

le on the center of the screen,

explorations, streams and references. To create artifacts, you mu

combo box, just as shown in figure 11.

Requesting the creation of new artifacts in Oracle Stream Explorer.

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

alue "CustomerData". Provide the tag "customer" in the

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

main and also drives you directly to the catalog screen. The catalog screen can also be accessed

button, found on the top right corner of the home screen.

e Stream Explorer are created and maintained. It displays all artifacts

le on the center of the screen, allowing the filtering of specific artifacts such as

explorations, streams and references. To create artifacts, you must request the creation of a new one in the

Requesting the creation of new artifacts in Oracle Stream Explorer.

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

alue "CustomerData". Provide the tag "customer" in the Tags field and from the

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The ho

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

main and also drives you directly to the catalog screen. The catalog screen can also be accessed

button, found on the top right corner of the home screen.

e Stream Explorer are created and maintained. It displays all artifacts

allowing the filtering of specific artifacts such as

st request the creation of a new one in the

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

field and from the

box, choose the option "Database Table". Figure 12 shows the first step of this wizard.

After getting authenticated to Oracle Stream Explorer, the home screen will be displayed. The home screen provides

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

main and also drives you directly to the catalog screen. The catalog screen can also be accessed

button, found on the top right corner of the home screen.

e Stream Explorer are created and maintained. It displays all artifacts

allowing the filtering of specific artifacts such as

st request the creation of a new one in the

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the

field and from the Source Type

me screen provides

a welcome message and displays a set of fancy images, each one inside a square that represents the common

business domains where event processing applications can be used. Clicking into one of these images sets the tag

main and also drives you directly to the catalog screen. The catalog screen can also be accessed

button, found on the top right corner of the home screen.

e Stream Explorer are created and maintained. It displays all artifacts

allowing the filtering of specific artifacts such as

st request the creation of a new one in the Create

The first artifact that will be created is the customer reference. Request the creation of a new reference as shown in

figure 11, selecting the option "Reference" in the list. The new reference creation wizard will then start. In the Name

Source Type combo

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14

Figure 12

Click in

database connection pool that will be used to connect to the database

choose the option "jdbc/minorityReport" as

Figure 13

In the third and last step,

the

button to finish the new reference creation wizard.

14 | INTRODUCTION TO THE

Figure 12. First step of the new reference creation wizard.

Click in the Next

database connection pool that will be used to connect to the database

choose the option "jdbc/minorityReport" as

Figure 13. Second step of the new reference creation wizard.

In the third and last step,

the Select Shape

button to finish the new reference creation wizard.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

First step of the new reference creation wizard.

Next button to proceed to the second step of the wizard.

database connection pool that will be used to connect to the database

choose the option "jdbc/minorityReport" as

Second step of the new reference creation wizard.

In the third and last step, the wizard will ask

Select Shape combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the

button to finish the new reference creation wizard.

ORACLE STREAM EXPLORER

First step of the new reference creation wizard.

button to proceed to the second step of the wizard.

database connection pool that will be used to connect to the database

choose the option "jdbc/minorityReport" as shown in figure 13. Click in the

Second step of the new reference creation wizard.

the wizard will ask for the name of the database table that the reference should point to

combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the

button to finish the new reference creation wizard.

First step of the new reference creation wizard.

button to proceed to the second step of the wizard.

database connection pool that will be used to connect to the database

shown in figure 13. Click in the

Second step of the new reference creation wizard.

for the name of the database table that the reference should point to

combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the

button to finish the new reference creation wizard.

button to proceed to the second step of the wizard. The wizard will

database connection pool that will be used to connect to the database server.

shown in figure 13. Click in the Next

for the name of the database table that the reference should point to

combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the

The wizard will ask for the name of the

server. In the Data Source Name

Next button to proceed to the third step.

for the name of the database table that the reference should point to

combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the

ask for the name of the

Data Source Name combo box,

button to proceed to the third step.

for the name of the database table that the reference should point to

combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the

ask for the name of the

combo box,

button to proceed to the third step.

for the name of the database table that the reference should point to. In

combo box, choose the option "CUSTOMER_DATA" as shown in figure 14. Click in the Create

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15

Figure 14

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

reference, set the

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

references.

Figure 15

Now it

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

creati

wizard will then start.

15 | INTRODUCTION TO THE

Figure 14. Third step of the new reference creation wizard.

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

reference, set the

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

references.

Figure 15. Oracle Stream Explorer showing the two cre

Now it is necessary the creation of a

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

creation of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

wizard will then start.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Third step of the new reference creation wizard.

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

reference, set the Name field to "CustomGreeting" and

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

Oracle Stream Explorer showing the two cre

is necessary the creation of a

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

wizard will then start.

ORACLE STREAM EXPLORER

Third step of the new reference creation wizard.

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

ield to "CustomGreeting" and

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

Oracle Stream Explorer showing the two cre

is necessary the creation of a third source,

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

Third step of the new reference creation wizard.

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

ield to "CustomGreeting" and the shape selected must be the

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

Oracle Stream Explorer showing the two created references.

source, but this time it will be a stream instead of a reference. This stream

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

the shape selected must be the

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

ated references.

but this time it will be a stream instead of a reference. This stream

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

the shape selected must be the "CUSTOM_GREETING"

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

but this time it will be a stream instead of a reference. This stream

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

"CUSTOM_GREETING"

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

but this time it will be a stream instead of a reference. This stream

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

After the completion of the wizard, a new shape named "CustomerData" should be listed in the table in the center of

the screen. Now, the same steps must be followed to create the second reference. During the creation of the second

"CUSTOM_GREETING"

database table. Figure 15 shows what should be exhibited in the catalog screen after the creation of these two

but this time it will be a stream instead of a reference. This stream

represents the incoming flow of eye scans containing the person's retina reading and its location. Request the

on of a new stream as shown in figure 11, selecting the option "Stream" in the list. The new stream creation

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16

In the

Source Type

unchecked, since all the explorations are going to be created in the sequence. Click in the

the second step of the wizard. Since the sel

source path of the CSV file. Click in the

Explorer.

Click the

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make s

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

finish the new stream creation wizard.

Figure 16

All

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

that detects whe

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

figure 11, selecting the option "Exploration

In the

from the

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

exploration as shown in figure 18.

16 | INTRODUCTION TO THE

In the Name field enter the value "EyeScanStream". Provide the tag "customer" in the

Source Type co

unchecked, since all the explorations are going to be created in the sequence. Click in the

the second step of the wizard. Since the sel

source path of the CSV file. Click in the

Explorer.

Click the Next button to proceed to the third step. In the

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make s

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

finish the new stream creation wizard.

Figure 16. All the three steps from the new stream creation wizard.

All sources needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

that detects whe

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

figure 11, selecting the option "Exploration

In the Name field enter the value "All Customers Near the Store". Provide the tag "customer" in the

from the Source

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

exploration as shown in figure 18.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

field enter the value "EyeScanStream". Provide the tag "customer" in the

combo box, select the option "CSV File". Leave the

unchecked, since all the explorations are going to be created in the sequence. Click in the

the second step of the wizard. Since the sel

source path of the CSV file. Click in the

button to proceed to the third step. In the

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make s

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

finish the new stream creation wizard.

All the three steps from the new stream creation wizard.

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

that detects when some person is

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

figure 11, selecting the option "Exploration

field enter the value "All Customers Near the Store". Provide the tag "customer" in the

Source combo box, choose the option "EyeScanStream", just as sho

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

exploration as shown in figure 18.

ORACLE STREAM EXPLORER

field enter the value "EyeScanStream". Provide the tag "customer" in the

mbo box, select the option "CSV File". Leave the

unchecked, since all the explorations are going to be created in the sequence. Click in the

the second step of the wizard. Since the selected source type was set to CSV, the wizard will then ask for the

source path of the CSV file. Click in the Upload File

button to proceed to the third step. In the

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make s

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

finish the new stream creation wizard.

All the three steps from the new stream creation wizard.

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

n some person is walking near the store, also identifying

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

figure 11, selecting the option "Exploration" in the list. The new exploration creation wizard will then start.

field enter the value "All Customers Near the Store". Provide the tag "customer" in the

combo box, choose the option "EyeScanStream", just as sho

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

exploration as shown in figure 18.

field enter the value "EyeScanStream". Provide the tag "customer" in the

mbo box, select the option "CSV File". Leave the

unchecked, since all the explorations are going to be created in the sequence. Click in the

ected source type was set to CSV, the wizard will then ask for the

Upload File button to select and upload the CSV file to Oracle Stream

button to proceed to the third step. In the Name

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make s

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

All the three steps from the new stream creation wizard.

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

near the store, also identifying

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

" in the list. The new exploration creation wizard will then start.

field enter the value "All Customers Near the Store". Provide the tag "customer" in the

combo box, choose the option "EyeScanStream", just as sho

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

field enter the value "EyeScanStream". Provide the tag "customer" in the

mbo box, select the option "CSV File". Leave the Create Exploration with this Source

unchecked, since all the explorations are going to be created in the sequence. Click in the

ected source type was set to CSV, the wizard will then ask for the

button to select and upload the CSV file to Oracle Stream

Name field enter the value "EyeScanStream" again. During

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make s

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

near the store, also identifying them

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

" in the list. The new exploration creation wizard will then start.

field enter the value "All Customers Near the Store". Provide the tag "customer" in the

combo box, choose the option "EyeScanStream", just as sho

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

field enter the value "EyeScanStream". Provide the tag "customer" in the Tags

Create Exploration with this Source

unchecked, since all the explorations are going to be created in the sequence. Click in the Next

ected source type was set to CSV, the wizard will then ask for the

button to select and upload the CSV file to Oracle Stream

ld enter the value "EyeScanStream" again. During

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

order to be able to present them in the third step of the wizard. For this reason, make sure if the

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

them against the customer reference. This

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

" in the list. The new exploration creation wizard will then start.

field enter the value "All Customers Near the Store". Provide the tag "customer" in the

combo box, choose the option "EyeScanStream", just as shown in figure 17. Click in the

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

Tags field and from the

Create Exploration with this Source checkbox

Next button to proceed to

ected source type was set to CSV, the wizard will then ask for the

button to select and upload the CSV file to Oracle Stream

ld enter the value "EyeScanStream" again. During

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

ure if the Manual Mapping

field is selected, and if the attributes shown matches with what is shown in figure 16. Click in the Create button to

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

against the customer reference. This

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

" in the list. The new exploration creation wizard will then start.

field enter the value "All Customers Near the Store". Provide the tag "customer" in the Tags field and

wn in figure 17. Click in the

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

field and from the

checkbox

button to proceed to

ected source type was set to CSV, the wizard will then ask for the

button to select and upload the CSV file to Oracle Stream

ld enter the value "EyeScanStream" again. During

the file upload process, the wizard parses the CSV file trying to detect the attribute names and their data types, in

Manual Mapping

button to

needed to start the implementation of the case study are now created. Now it is time to start the creation

of the explorations, the artifacts that will provide the expected behavior. The first exploration to be built is the one

against the customer reference. This

exploration will be named "All Customers Near the Store". Request the creation of a new exploration as shown in

field and

wn in figure 17. Click in the Create

button to finish the new exploration creation wizard. The exploration editor will then open with the newly created

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17

Figure 17

The first thing noticed in the exploration editor is that the

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

the exploration is

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

coming from the sources.

Figure 18

17 | INTRODUCTION TO THE

Figure 17. Setting the source in the new exploration creation wizar

The first thing noticed in the exploration editor is that the

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

the exploration is

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

coming from the sources.

Figure 18. Newly created exploration presenting in real

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Setting the source in the new exploration creation wizar

The first thing noticed in the exploration editor is that the

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

the exploration is changed, these sections are also updated, in real

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

coming from the sources.

created exploration presenting in real

ORACLE STREAM EXPLORER

Setting the source in the new exploration creation wizar

The first thing noticed in the exploration editor is that the

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

changed, these sections are also updated, in real

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

created exploration presenting in real

Setting the source in the new exploration creation wizard.

The first thing noticed in the exploration editor is that the Live Output Stream

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

changed, these sections are also updated, in real

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

created exploration presenting in real-time the data coming from the sources.

Live Output Stream and the

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

changed, these sections are also updated, in real-time, helping the user to have a better view of

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

time the data coming from the sources.

and the Charts

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

time, helping the user to have a better view of

what the output result looks like. Figure 18 shows the newly created exploration presenting in real

time the data coming from the sources.

sections shows in real

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

time, helping the user to have a better view of

what the output result looks like. Figure 18 shows the newly created exploration presenting in real-time the data

sections shows in real-

time the results coming from the sources, which in this case is the eye scan stream. This is interesting because as

time, helping the user to have a better view of

time the data

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18

Since the exploration need

need to be created. In the

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

are looking for. For this exploration, the filters w

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

shows these two filters being created in Oracle Stream Explorer.

Figure 19

After setting these filters, the

the criteria, and the

presents only approximate values for the latitude and longitude attributes.

In order to identify who each person is, it is necessary that every eve

the customer reference that holds the details of the customer. The first step is

exploration to include another shape. Click on the

the

Correlations

Click in the

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

Live Output Stream

18 | INTRODUCTION TO THE

Since the exploration need

need to be created. In the

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

are looking for. For this exploration, the filters w

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

shows these two filters being created in Oracle Stream Explorer.

Figure 19. Filtering the person's location in Oracle Stream Explorer.

After setting these filters, the

the criteria, and the

presents only approximate values for the latitude and longitude attributes.

In order to identify who each person is, it is necessary that every eve

the customer reference that holds the details of the customer. The first step is

exploration to include another shape. Click on the

the drop down list. When two or more shapes are used as sources in an exploration, a new section called

Correlations start

Click in the Add a Correlation

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

Live Output Stream

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Since the exploration needs to detect only persons that are near the store, filters to pick up only the ones o

need to be created. In the Filters

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

are looking for. For this exploration, the filters w

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

shows these two filters being created in Oracle Stream Explorer.

Filtering the person's location in Oracle Stream Explorer.

After setting these filters, the Live

the criteria, and the Charts section will then start to show a more flat graph, since the live output stream now

presents only approximate values for the latitude and longitude attributes.

In order to identify who each person is, it is necessary that every eve

the customer reference that holds the details of the customer. The first step is

exploration to include another shape. Click on the

drop down list. When two or more shapes are used as sources in an exploration, a new section called

starts to appear in the exploration editor.

Add a Correlation link to create a new correlation between the sources. From the left

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

Live Output Stream section will show attributes from both sources, as shown in figure 20.

ORACLE STREAM EXPLORER

to detect only persons that are near the store, filters to pick up only the ones o

ilters section, click in the

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

are looking for. For this exploration, the filters w

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

shows these two filters being created in Oracle Stream Explorer.

Filtering the person's location in Oracle Stream Explorer.

ive Output Stream

section will then start to show a more flat graph, since the live output stream now

presents only approximate values for the latitude and longitude attributes.

In order to identify who each person is, it is necessary that every eve

the customer reference that holds the details of the customer. The first step is

exploration to include another shape. Click on the

drop down list. When two or more shapes are used as sources in an exploration, a new section called

pear in the exploration editor.

link to create a new correlation between the sources. From the left

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

section will show attributes from both sources, as shown in figure 20.

to detect only persons that are near the store, filters to pick up only the ones o

section, click in the Add a Filter

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

are looking for. For this exploration, the filters will guarantee that only persons in which its location has the latitude

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

shows these two filters being created in Oracle Stream Explorer.

Filtering the person's location in Oracle Stream Explorer.

tream section will automatically discard any event that does not satisfies

section will then start to show a more flat graph, since the live output stream now

presents only approximate values for the latitude and longitude attributes.

In order to identify who each person is, it is necessary that every eve

the customer reference that holds the details of the customer. The first step is

exploration to include another shape. Click on the Sources section and select the "CustomerData" reference from

drop down list. When two or more shapes are used as sources in an exploration, a new section called

pear in the exploration editor.

link to create a new correlation between the sources. From the left

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

section will show attributes from both sources, as shown in figure 20.

to detect only persons that are near the store, filters to pick up only the ones o

Add a Filter link. A new filter entry will be created where you

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

ill guarantee that only persons in which its location has the latitude

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

shows these two filters being created in Oracle Stream Explorer.

will automatically discard any event that does not satisfies

section will then start to show a more flat graph, since the live output stream now

presents only approximate values for the latitude and longitude attributes.

In order to identify who each person is, it is necessary that every event from the eye scan str

the customer reference that holds the details of the customer. The first step is

section and select the "CustomerData" reference from

drop down list. When two or more shapes are used as sources in an exploration, a new section called

link to create a new correlation between the sources. From the left

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

section will show attributes from both sources, as shown in figure 20.

to detect only persons that are near the store, filters to pick up only the ones o

link. A new filter entry will be created where you

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

ill guarantee that only persons in which its location has the latitude

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "

will automatically discard any event that does not satisfies

section will then start to show a more flat graph, since the live output stream now

from the eye scan str

the customer reference that holds the details of the customer. The first step is to change the

section and select the "CustomerData" reference from

drop down list. When two or more shapes are used as sources in an exploration, a new section called

link to create a new correlation between the sources. From the left

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

section will show attributes from both sources, as shown in figure 20.

to detect only persons that are near the store, filters to pick up only the ones of

link. A new filter entry will be created where you

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

ill guarantee that only persons in which its location has the latitude

attribute greater than or equals to "20.00" and the longitude attribute is less than or equals to "-60.00". Figure 19

will automatically discard any event that does not satisfies

section will then start to show a more flat graph, since the live output stream now

from the eye scan stream be correlated with

change the Sources section of the

section and select the "CustomerData" reference from

drop down list. When two or more shapes are used as sources in an exploration, a new section called

link to create a new correlation between the sources. From the left side choose the

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

interest

link. A new filter entry will be created where you

can choose which attribute to handle, which operator to use and set which value restricts the output result that you

ill guarantee that only persons in which its location has the latitude

60.00". Figure 19

will automatically discard any event that does not satisfies

section will then start to show a more flat graph, since the live output stream now

eam be correlated with

section of the

section and select the "CustomerData" reference from

drop down list. When two or more shapes are used as sources in an exploration, a new section called

side choose the

"eyeScan" attribute and from the right side choose the "scan_entry" attribute. Once the correlation is configured, the

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19

Figure 20

In order to be considered complete, explorations need to provide the expected output result since once published,

they

output result via

and "gender", also re

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

shown in figure 21.

Figure 21

19 | INTRODUCTION TO THE

Figure 20. Result of the

In order to be considered complete, explorations need to provide the expected output result since once published,

they may be used in the list of sources of other explorations. Oracl

output result via

and "gender", also re

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

shown in figure 21.

Figure 21. Customizing the output result of an exploration before publishing it.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Result of the event correlation between the eye scan stream and the customer reference.

In order to be considered complete, explorations need to provide the expected output result since once published,

be used in the list of sources of other explorations. Oracl

output result via the Properties

and "gender", also re-ordering them to be the first and second attributes of the propertie

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

shown in figure 21.

Customizing the output result of an exploration before publishing it.

ORACLE STREAM EXPLORER

event correlation between the eye scan stream and the customer reference.

In order to be considered complete, explorations need to provide the expected output result since once published,

be used in the list of sources of other explorations. Oracl

link. Access the

ordering them to be the first and second attributes of the propertie

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

Customizing the output result of an exploration before publishing it.

event correlation between the eye scan stream and the customer reference.

In order to be considered complete, explorations need to provide the expected output result since once published,

be used in the list of sources of other explorations. Oracl

Access the Properties link

ordering them to be the first and second attributes of the propertie

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

Customizing the output result of an exploration before publishing it.

event correlation between the eye scan stream and the customer reference.

In order to be considered complete, explorations need to provide the expected output result since once published,

be used in the list of sources of other explorations. Oracle Stream Explorer allows the mo

link and leave selected only the attributes "last_name"

ordering them to be the first and second attributes of the propertie

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

Customizing the output result of an exploration before publishing it.

event correlation between the eye scan stream and the customer reference.

In order to be considered complete, explorations need to provide the expected output result since once published,

e Stream Explorer allows the mo

selected only the attributes "last_name"

ordering them to be the first and second attributes of the properties section. After this, double

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

In order to be considered complete, explorations need to provide the expected output result since once published,

e Stream Explorer allows the modification of the

selected only the attributes "last_name"

s section. After this, double

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

In order to be considered complete, explorations need to provide the expected output result since once published,

dification of the

selected only the attributes "last_name"

s section. After this, double-

click the "last_name" attribute to provide a custom display name for it, setting its value to "customerName", as

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20

The explor

labeled

possible action that can be performed in the

Figure 22

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

as shown in figure 11, s

start.

In the

and from the

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

showing in the

and select the "CustomGreeting" reference from the drop down list. Once the

the exploration editor, click on the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

is configured, the

20 | INTRODUCTION TO THE

The exploration is now ready to be published. In the top right corner of the exploration editor there is a button

labeled Actions

possible action that can be performed in the

Figure 22. Publishing the exploration to be available as a stream.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

as shown in figure 11, s

start.

In the Name field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the

and from the Source

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

showing in the Live Output Stream

and select the "CustomGreeting" reference from the drop down list. Once the

the exploration editor, click on the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

is configured, the

INTRODUCTION TO THE ORACLE STREAM EXPLOR

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

possible action that can be performed in the

Publishing the exploration to be available as a stream.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

as shown in figure 11, selecting the option "Exploration" in the list. The new exploration creation wizard will then

field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the

Source combo box, choose the option "All Customers Near the Store". Click the

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

Live Output Stream

and select the "CustomGreeting" reference from the drop down list. Once the

the exploration editor, click on the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

is configured, the Live Output Stream

ORACLE STREAM EXPLORER

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

possible action that can be performed in the exploration. Click in the

Publishing the exploration to be available as a stream.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

electing the option "Exploration" in the list. The new exploration creation wizard will then

field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the

combo box, choose the option "All Customers Near the Store". Click the

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

Live Output Stream section the out

and select the "CustomGreeting" reference from the drop down list. Once the

the exploration editor, click on the Add a Correlation

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

Live Output Stream section will show attributes from both sources, as

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

exploration. Click in the

Publishing the exploration to be available as a stream.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

electing the option "Exploration" in the list. The new exploration creation wizard will then

field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the

combo box, choose the option "All Customers Near the Store". Click the

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

section the output result from the first exploration. Click in the

and select the "CustomGreeting" reference from the drop down list. Once the

Add a Correlation link to create a

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

section will show attributes from both sources, as

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

exploration. Click in the Publish button just as shown in figure 22.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

electing the option "Exploration" in the list. The new exploration creation wizard will then

field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the

combo box, choose the option "All Customers Near the Store". Click the

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

put result from the first exploration. Click in the

and select the "CustomGreeting" reference from the drop down list. Once the Correlations

link to create a new correlation between the sources. From the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

section will show attributes from both sources, as

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

button just as shown in figure 22.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

electing the option "Exploration" in the list. The new exploration creation wizard will then

field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the

combo box, choose the option "All Customers Near the Store". Click the

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

put result from the first exploration. Click in the

Correlations section starts to appear in

new correlation between the sources. From the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

section will show attributes from both sources, as shown in figure 23.

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

button just as shown in figure 22.

With the first exploration properly published, the second exploration can start to be built. The second exploration wi

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

electing the option "Exploration" in the list. The new exploration creation wizard will then

field enter the value "Greetings for All Customers Near". Provide the tag "customer" in the Tags

combo box, choose the option "All Customers Near the Store". Click the Create button to finish

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

put result from the first exploration. Click in the Sources

section starts to appear in

new correlation between the sources. From the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

shown in figure 23.

ation is now ready to be published. In the top right corner of the exploration editor there is a button

that once clicked opens a suspended menu with other buttons, each one of them related to a

button just as shown in figure 22.

With the first exploration properly published, the second exploration can start to be built. The second exploration will

create greetings for each customer near the store, using the person gender to detect which appropriate greeting to

use. This exploration will be named "Greetings for All Customers Near". Request the creation of a new exploration

electing the option "Exploration" in the list. The new exploration creation wizard will then

Tags field

button to finish

the new exploration creation wizard. The exploration editor will then open with the newly created exploration,

section

section starts to appear in

new correlation between the sources. From the

left side choose the "gender" attribute and from the right side choose the "gender" attribute too. Once the correlation

Page 22: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

21

Figure 23

To

back

setting the value of the "greeting_code" attribute to be equals to "WBACK".

Just like it was done with the first exploration, the output result of this second exploration need

Access the

"custom_message", also re

Still in the

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

just as shown in figure 24.

21 | INTRODUCTION TO THE

Figure 23. Second exploration with the correlation already in place.

To avoid unnecessary greetings for the

back to the GAP" greeting is used. In the

setting the value of the "greeting_code" attribute to be equals to "WBACK".

Just like it was done with the first exploration, the output result of this second exploration need

Access the Properties

"custom_message", also re

Still in the Properti

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

just as shown in figure 24.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Second exploration with the correlation already in place.

avoid unnecessary greetings for the

to the GAP" greeting is used. In the

setting the value of the "greeting_code" attribute to be equals to "WBACK".

Just like it was done with the first exploration, the output result of this second exploration need

roperties link and leave selected only the attributes "ice_break_message", "customerName" and the

"custom_message", also re-ordering them to be the first, second and the third attributes of the pr

roperties link, provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

just as shown in figure 24.

ORACLE STREAM EXPLORER

Second exploration with the correlation already in place.

avoid unnecessary greetings for the customers, a filter needs to be created to establish that only the "Welc

to the GAP" greeting is used. In the Filters

setting the value of the "greeting_code" attribute to be equals to "WBACK".

Just like it was done with the first exploration, the output result of this second exploration need

and leave selected only the attributes "ice_break_message", "customerName" and the

ordering them to be the first, second and the third attributes of the pr

provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

Second exploration with the correlation already in place.

customers, a filter needs to be created to establish that only the "Welc

ilters section, click in the

setting the value of the "greeting_code" attribute to be equals to "WBACK".

Just like it was done with the first exploration, the output result of this second exploration need

and leave selected only the attributes "ice_break_message", "customerName" and the

ordering them to be the first, second and the third attributes of the pr

provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

customers, a filter needs to be created to establish that only the "Welc

section, click in the Add a Filter

setting the value of the "greeting_code" attribute to be equals to "WBACK".

Just like it was done with the first exploration, the output result of this second exploration need

and leave selected only the attributes "ice_break_message", "customerName" and the

ordering them to be the first, second and the third attributes of the pr

provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

customers, a filter needs to be created to establish that only the "Welc

Add a Filter link to create a new filter

Just like it was done with the first exploration, the output result of this second exploration need

and leave selected only the attributes "ice_break_message", "customerName" and the

ordering them to be the first, second and the third attributes of the pr

provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

customers, a filter needs to be created to establish that only the "Welc

link to create a new filter

Just like it was done with the first exploration, the output result of this second exploration needs to be customized.

and leave selected only the attributes "ice_break_message", "customerName" and the

ordering them to be the first, second and the third attributes of the properties section.

provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

customers, a filter needs to be created to establish that only the "Welcome

link to create a new filter entry,

tomized.

and leave selected only the attributes "ice_break_message", "customerName" and the

operties section.

provide custom display names for each one of these three attributes, setting the first

attribute to "iceBreakMessage", the second attribute to "customerName" and the third attribute to "customMessage",

Page 23: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

22

Figure 24

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

Stream Explorer detecting in real

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

salutations

However, while it is interesting to have the ou

editor, this can be considered useless if this output result could not be sent to an external system that handles the

greeting notification. Fortunately, Oracle Stream Explorer allows th

external systems via the

Explorer:

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

the configured target. This means that

immediately without

situation became relevant, allowing a truly event

F

MDB (Message

via the

of a target of this type, as shown in figure 25.

22 | INTRODUCTION TO THE

Figure 24. Second exploration with its output result properly customized.

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

Stream Explorer detecting in real

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

salutations such as "Mr." and "Mrs." before saying their names.

However, while it is interesting to have the ou

editor, this can be considered useless if this output result could not be sent to an external system that handles the

greeting notification. Fortunately, Oracle Stream Explorer allows th

external systems via the

Explorer:

» CSV File

» HTTP Publisher

» Event-Driven Network

» Java Message Service

» REST Endpoints

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

the configured target. This means that

immediately without

situation became relevant, allowing a truly event

For instance, imagine that a voice

MDB (Message-

via the Java Speech API

of a target of this type, as shown in figure 25.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Second exploration with its output result properly customized.

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

Stream Explorer detecting in real

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

such as "Mr." and "Mrs." before saying their names.

However, while it is interesting to have the ou

editor, this can be considered useless if this output result could not be sent to an external system that handles the

greeting notification. Fortunately, Oracle Stream Explorer allows th

external systems via the Configure a Target

CSV File: Write the output results to a CSV file, allowing

HTTP Publisher: Publishes the output results to an outbound HTTP channel from OEP.

Driven Network: Send the output results to SOA Suite EDN

Java Message Service: Creates a

REST Endpoints: Performs a HTTP

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

the configured target. This means that

immediately without any delays. This approach allows that actions can be taken in the exact moment in which the

situation became relevant, allowing a truly event

or instance, imagine that a voice

-Driven Bean) to consume messages from the JMS destination and execute the greetings speech

Java Speech API. The output results from the exploration could be easily sent via JMS by the configuration

of a target of this type, as shown in figure 25.

ORACLE STREAM EXPLORER

Second exploration with its output result properly customized.

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

Stream Explorer detecting in real-time when someone is near the store and

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

such as "Mr." and "Mrs." before saying their names.

However, while it is interesting to have the output result correctly shown in the Oracle Stream Explorer exploration

editor, this can be considered useless if this output result could not be sent to an external system that handles the

greeting notification. Fortunately, Oracle Stream Explorer allows th

Configure a Target button. The following targets are currently supported in Oracle Stream

: Write the output results to a CSV file, allowing

: Publishes the output results to an outbound HTTP channel from OEP.

: Send the output results to SOA Suite EDN

: Creates a javax.jms.MapMessage

: Performs a HTTP POST with the output results into a REST endpoint.

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

the configured target. This means that once a new output result is available i

delays. This approach allows that actions can be taken in the exact moment in which the

situation became relevant, allowing a truly event

or instance, imagine that a voice-enabled system implemented in Java listen greeting requests via JMS, using a

Driven Bean) to consume messages from the JMS destination and execute the greetings speech

. The output results from the exploration could be easily sent via JMS by the configuration

of a target of this type, as shown in figure 25.

Second exploration with its output result properly customized.

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

n someone is near the store and

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

such as "Mr." and "Mrs." before saying their names.

tput result correctly shown in the Oracle Stream Explorer exploration

editor, this can be considered useless if this output result could not be sent to an external system that handles the

greeting notification. Fortunately, Oracle Stream Explorer allows th

button. The following targets are currently supported in Oracle Stream

: Write the output results to a CSV file, allowing

: Publishes the output results to an outbound HTTP channel from OEP.

: Send the output results to SOA Suite EDN

javax.jms.MapMessage

POST with the output results into a REST endpoint.

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

once a new output result is available i

delays. This approach allows that actions can be taken in the exact moment in which the

situation became relevant, allowing a truly event-driven approach.

enabled system implemented in Java listen greeting requests via JMS, using a

Driven Bean) to consume messages from the JMS destination and execute the greetings speech

. The output results from the exploration could be easily sent via JMS by the configuration

Second exploration with its output result properly customized.

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

n someone is near the store and greeti

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

such as "Mr." and "Mrs." before saying their names.

tput result correctly shown in the Oracle Stream Explorer exploration

editor, this can be considered useless if this output result could not be sent to an external system that handles the

greeting notification. Fortunately, Oracle Stream Explorer allows that output results from explorations to be sent to

button. The following targets are currently supported in Oracle Stream

: Write the output results to a CSV file, allowing the contents to be app

: Publishes the output results to an outbound HTTP channel from OEP.

: Send the output results to SOA Suite EDN-enabled subscriber.

javax.jms.MapMessage and send it to a JMS

POST with the output results into a REST endpoint.

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

once a new output result is available i

delays. This approach allows that actions can be taken in the exact moment in which the

driven approach.

enabled system implemented in Java listen greeting requests via JMS, using a

Driven Bean) to consume messages from the JMS destination and execute the greetings speech

. The output results from the exploration could be easily sent via JMS by the configuration

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

greeting them with a custom message.

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

tput result correctly shown in the Oracle Stream Explorer exploration

editor, this can be considered useless if this output result could not be sent to an external system that handles the

at output results from explorations to be sent to

button. The following targets are currently supported in Oracle Stream

contents to be appended.

: Publishes the output results to an outbound HTTP channel from OEP.

enabled subscriber.

and send it to a JMS destination.

POST with the output results into a REST endpoint.

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

once a new output result is available in Oracle Stream Explorer, it is

delays. This approach allows that actions can be taken in the exact moment in which the

enabled system implemented in Java listen greeting requests via JMS, using a

Driven Bean) to consume messages from the JMS destination and execute the greetings speech

. The output results from the exploration could be easily sent via JMS by the configuration

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

ng them with a custom message.

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

tput result correctly shown in the Oracle Stream Explorer exploration

editor, this can be considered useless if this output result could not be sent to an external system that handles the

at output results from explorations to be sent to

button. The following targets are currently supported in Oracle Stream

ended.

: Publishes the output results to an outbound HTTP channel from OEP.

enabled subscriber.

destination.

POST with the output results into a REST endpoint.

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

n Oracle Stream Explorer, it is

delays. This approach allows that actions can be taken in the exact moment in which the

enabled system implemented in Java listen greeting requests via JMS, using a

Driven Bean) to consume messages from the JMS destination and execute the greetings speech

. The output results from the exploration could be easily sent via JMS by the configuration

Publish this second exploration as shown in figure 22. The case study is now completely implemented, with Oracle

ng them with a custom message.

By using the person's gender to differentiate if it is a man or a woman, the automated approach also uses English

tput result correctly shown in the Oracle Stream Explorer exploration

editor, this can be considered useless if this output result could not be sent to an external system that handles the

at output results from explorations to be sent to

button. The following targets are currently supported in Oracle Stream

It is important to note that regardless of the chosen target type, the generated output results are sent continuously to

n Oracle Stream Explorer, it is sent

delays. This approach allows that actions can be taken in the exact moment in which the

enabled system implemented in Java listen greeting requests via JMS, using a

Driven Bean) to consume messages from the JMS destination and execute the greetings speech

. The output results from the exploration could be easily sent via JMS by the configuration

Page 24: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

23

Figure 25

The end

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

applications. Howeve

Speech API to synthesize the greeting using a human voice.

Conclusion

Can you imagine yourself driving your car by only looking

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

Oracle Stream Explorer is a web

to provide a tool for business users

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

to develop event processing

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

The script shown in the listing 1 creates these two database tables and a

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

unchanged.

--------------------------------------------------------

--

--------------------------------------------------------

23 | INTRODUCTION TO THE

Figure 25. Configuration of a JMS

The end-to-end implementation of a voice

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

applications. Howeve

Speech API to synthesize the greeting using a human voice.

Conclusion

Can you imagine yourself driving your car by only looking

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

Oracle Stream Explorer is a web

to provide a tool for business users

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

to develop event processing

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

The script shown in the listing 1 creates these two database tables and a

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

unchanged.

--------------------------------------------------------

-- DDL for the table CUSTOMER_DATA

--------------------------------------------------------

CREATE TABLE "CUSTOMER_DATA"

( "CUSTOMER_ID" INTEGER NOT NULL PRIMARY KEY,

"SCAN_ENTRY" VARCHAR2(255) NOT NULL,

"FIRST_NAME" VARCHAR2(20) NOT NULL,

"LAST_NAME" VARCHAR2(20) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL

);

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Configuration of a JMS-based target to send th

end implementation of a voice

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

applications. However, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

Speech API to synthesize the greeting using a human voice.

Can you imagine yourself driving your car by only looking

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

Oracle Stream Explorer is a web

to provide a tool for business users

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

to develop event processing-based applications through a step

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

The script shown in the listing 1 creates these two database tables and a

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

--------------------------------------------------------

DDL for the table CUSTOMER_DATA

--------------------------------------------------------

CREATE TABLE "CUSTOMER_DATA"

"CUSTOMER_ID" INTEGER NOT NULL PRIMARY KEY,

AN_ENTRY" VARCHAR2(255) NOT NULL,

"FIRST_NAME" VARCHAR2(20) NOT NULL,

"LAST_NAME" VARCHAR2(20) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL

ORACLE STREAM EXPLORER

based target to send th

end implementation of a voice-enabled system using Java is definitely out of the scope of this paper,

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

r, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

Speech API to synthesize the greeting using a human voice.

Can you imagine yourself driving your car by only looking

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

Oracle Stream Explorer is a web-based application that leverages the capabilities found in Oracle Event Processing

to provide a tool for business users to analyze streams o

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

based applications through a step

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

The script shown in the listing 1 creates these two database tables and a

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

--------------------------------------------------------

DDL for the table CUSTOMER_DATA

--------------------------------------------------------

CREATE TABLE "CUSTOMER_DATA"

"CUSTOMER_ID" INTEGER NOT NULL PRIMARY KEY,

AN_ENTRY" VARCHAR2(255) NOT NULL,

"FIRST_NAME" VARCHAR2(20) NOT NULL,

"LAST_NAME" VARCHAR2(20) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL

based target to send the output results.

enabled system using Java is definitely out of the scope of this paper,

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

r, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

Speech API to synthesize the greeting using a human voice.

Can you imagine yourself driving your car by only looking in the rear

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

based application that leverages the capabilities found in Oracle Event Processing

analyze streams of events in real

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

based applications through a step

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

The script shown in the listing 1 creates these two database tables and a

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

--------------------------------------------------------

--------------------------------------------------------

"CUSTOMER_ID" INTEGER NOT NULL PRIMARY KEY,

AN_ENTRY" VARCHAR2(255) NOT NULL,

"FIRST_NAME" VARCHAR2(20) NOT NULL,

"LAST_NAME" VARCHAR2(20) NOT NULL,

e output results.

enabled system using Java is definitely out of the scope of this paper,

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

r, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

the rear-mirror? This is how

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

based application that leverages the capabilities found in Oracle Event Processing

f events in real-time, empowering them to gain insight and

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

based applications through a step-by-step implementation of

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

The script shown in the listing 1 creates these two database tables and also populates it with sample data. The

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

--------------------------------------------------------

--------------------------------------------------------

"CUSTOMER_ID" INTEGER NOT NULL PRIMARY KEY,

enabled system using Java is definitely out of the scope of this paper,

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

r, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

mirror? This is how many

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

important opportunities or letting threats take advantage of our lack of awareness.

based application that leverages the capabilities found in Oracle Event Processing

time, empowering them to gain insight and

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

step implementation of a case stud

Appendix A: Scripts and Samples used in the Case Study

In order to be able to implement the case study presented in this paper, two database tables need to be created.

lso populates it with sample data. The

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names

enabled system using Java is definitely out of the scope of this paper,

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

r, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

many enterprises run their

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

based application that leverages the capabilities found in Oracle Event Processing

time, empowering them to gain insight and

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

a case study.

In order to be able to implement the case study presented in this paper, two database tables need to be created.

lso populates it with sample data. The

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

adjusts in the script can be performed, as long that the structure of the tables and the column names remains

enabled system using Java is definitely out of the scope of this paper,

which focuses on explaining the fundamentals of Oracle Stream Explorer and how to build event processing

r, the Appendix B of this paper contains an implementation of a MDB that leverages the Java

enterprises run their

business today, trying to get insight using traditional data warehouse and business intelligence technologies.

Looking at past information does not always provide the ability to react appropriately; this in turn leads to missing

based application that leverages the capabilities found in Oracle Event Processing

time, empowering them to gain insight and

take appropriate actions when needed. This paper explained the fundamentals of Oracle Stream Explorer and how

In order to be able to implement the case study presented in this paper, two database tables need to be created.

lso populates it with sample data. The

script was successfully tested against the Oracle database, but it may work well in other databases. If necessary,

remains

Page 25: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

24

--------------------------------------------------------

--

--------------------------------------------------------

--------------------------------------------------------

--

--------------------------------------------------------

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN

(1,'CB281A82

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(2,'B7C841D6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(3,'0028CF68

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(4,'B5C9DA94

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(5,'1EB943F4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,F

(6,'B71E107F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(7,'AF25D32D

Insert

(8,'EA712817

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(9,'27AFC1DC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(10,'45D8A1B8

Insert into CUSTOMER_DATA

(11,'3FAB4B01

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(12,'F6C6D0BC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(13,'096458CC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

(14,'95005563

24 | INTRODUCTION TO THE

--------------------------------------------------------

-- DDL for the table CUSTOM_GREETING

--------------------------------------------------------

CREATE TABLE "CUSTOM_GREETING"

( "GREETING_ID" INTEGER NOT NULL PRIMARY KEY,

"GREETING_CODE" VARCHAR2(5) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL,

"ICE_BREAK_MESSAGE" VARCHAR2(50) NOT NUL

"CUSTOM_MESSAGE" VARCHAR2(100) NOT NULL

);

--------------------------------------------------------

-- Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN

(1,'CB281A82-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(2,'B7C841D6-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(3,'0028CF68-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(4,'B5C9DA94-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(5,'1EB943F4-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,F

(6,'B71E107F-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(7,'AF25D32D-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(8,'EA712817-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(9,'27AFC1DC-

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(10,'45D8A1B8

Insert into CUSTOMER_DATA

(11,'3FAB4B01

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(12,'F6C6D0BC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(13,'096458CC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

(14,'95005563

INTRODUCTION TO THE ORACLE STREAM EXPLOR

--------------------------------------------------------

DDL for the table CUSTOM_GREETING

--------------------------------------------------------

CREATE TABLE "CUSTOM_GREETING"

"GREETING_ID" INTEGER NOT NULL PRIMARY KEY,

"GREETING_CODE" VARCHAR2(5) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL,

"ICE_BREAK_MESSAGE" VARCHAR2(50) NOT NUL

"CUSTOM_MESSAGE" VARCHAR2(100) NOT NULL

--------------------------------------------------------

Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN

-EBF9-247F-8D4C

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-DC7D-0C32-1402

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-2264-D591-C3F8

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-6AF7-9BFF-19F5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-C7B3-29D8-D0D3

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,F

-DD89-76EC-497B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-0870-7C79-5ECE

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-9B59-042A-F952

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-74D6-D988-9E7E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(10,'45D8A1B8-72DB-A6EE-7725

Insert into CUSTOMER_DATA

(11,'3FAB4B01-AA8C-CA14-03B7

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(12,'F6C6D0BC-5772-97BA-2EB3

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(13,'096458CC-3F58-0CFC-08B9

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

(14,'95005563-5DFB-2F6D-5B12

ORACLE STREAM EXPLORER

--------------------------------------------------------

DDL for the table CUSTOM_GREETING

--------------------------------------------------------

CREATE TABLE "CUSTOM_GREETING"

"GREETING_ID" INTEGER NOT NULL PRIMARY KEY,

"GREETING_CODE" VARCHAR2(5) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL,

"ICE_BREAK_MESSAGE" VARCHAR2(50) NOT NUL

"CUSTOM_MESSAGE" VARCHAR2(100) NOT NULL

--------------------------------------------------------

Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN

8D4C-632F2CD4DC9E','Gregory','Berger','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1402-58E0E06F0C74','George','Griffith

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C3F8-EECF78E3635F','Damian','Key','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

19F5-9EECC4797417','Scott','Bridges','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D0D3-042507CCDD50','Colton','Kinney','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,F

497B-9F3BB81CC790','Brody','Goodman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5ECE-A8D88E63A099','Simon','Park','M');

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F952-4BD9B2621FE7','Addison','Medina','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9E7E-35522FD65CAC','Charles','Sutton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7725-4AA8F379FA2E','Vaughan','Lawson','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

03B7-69F75670D0C2','Felix','Haley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2EB3-E0A6DE91D64B','M

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

08B9-08E2134AFCB7','Brent','Faulkner','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

5B12-1BB0DEE71492','Kasimir','Fitzgerald','F');

--------------------------------------------------------

DDL for the table CUSTOM_GREETING

--------------------------------------------------------

"GREETING_ID" INTEGER NOT NULL PRIMARY KEY,

"GREETING_CODE" VARCHAR2(5) NOT NULL,

"GENDER" VARCHAR2(1) NOT NULL,

"ICE_BREAK_MESSAGE" VARCHAR2(50) NOT NULL,

"CUSTOM_MESSAGE" VARCHAR2(100) NOT NULL

--------------------------------------------------------

Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

632F2CD4DC9E','Gregory','Berger','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

58E0E06F0C74','George','Griffith

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EECF78E3635F','Damian','Key','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9EECC4797417','Scott','Bridges','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

042507CCDD50','Colton','Kinney','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,F

9F3BB81CC790','Brody','Goodman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A8D88E63A099','Simon','Park','M');

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4BD9B2621FE7','Addison','Medina','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

35522FD65CAC','Charles','Sutton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4AA8F379FA2E','Vaughan','Lawson','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

69F75670D0C2','Felix','Haley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E0A6DE91D64B','Mason','Mclean','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

08E2134AFCB7','Brent','Faulkner','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

1BB0DEE71492','Kasimir','Fitzgerald','F');

--------------------------------------------------------

--------------------------------------------------------

"GREETING_ID" INTEGER NOT NULL PRIMARY KEY,

L,

--------------------------------------------------------

--------------------------------------------------------

_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

632F2CD4DC9E','Gregory','Berger','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

58E0E06F0C74','George','Griffith','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EECF78E3635F','Damian','Key','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9EECC4797417','Scott','Bridges','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

042507CCDD50','Colton','Kinney','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9F3BB81CC790','Brody','Goodman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A8D88E63A099','Simon','Park','M');

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4BD9B2621FE7','Addison','Medina','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

35522FD65CAC','Charles','Sutton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4AA8F379FA2E','Vaughan','Lawson','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

69F75670D0C2','Felix','Haley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

ason','Mclean','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

08E2134AFCB7','Brent','Faulkner','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

1BB0DEE71492','Kasimir','Fitzgerald','F');

_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

632F2CD4DC9E','Gregory','Berger','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EECF78E3635F','Damian','Key','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9EECC4797417','Scott','Bridges','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

042507CCDD50','Colton','Kinney','F');

IRST_NAME,LAST_NAME,GENDER) values

9F3BB81CC790','Brody','Goodman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A8D88E63A099','Simon','Park','M');

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4BD9B2621FE7','Addison','Medina','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

35522FD65CAC','Charles','Sutton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4AA8F379FA2E','Vaughan','Lawson','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

69F75670D0C2','Felix','Haley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

ason','Mclean','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

08E2134AFCB7','Brent','Faulkner','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) va

1BB0DEE71492','Kasimir','Fitzgerald','F');

_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

IRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

IRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

lues

Page 26: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

25

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(15,'F812B9FD

Insert into CUSTOMER_DATA (

(16,'5F8F72F2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(17,'C54C5779

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(18,'D1EB489E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

(19,'50035120

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(20,'F35FB6B7

Insert into CUSTOMER_DATA (CUSTO

(21,'DBA752B4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(22,'23E5896B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(23,'480ED76A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(24,'FF7A36A3

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(25,'E083DDBE

Insert into CUSTOMER_DATA (CUSTOMER

(26,'B7E4A6AD

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(27,'3BB44589

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(28,'D929DC13

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(30,'B8D4A245

Insert into CUSTOMER_DATA (CUSTOMER_ID,S

(31,'BFD9B89D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(32,'FC1E3D87

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(33,'60473650

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(34,'F44E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(35,'77F7D210

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCA

(36,'2DE41CE6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(37,'0A4B9BF9

25 | INTRODUCTION TO THE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(15,'F812B9FD

Insert into CUSTOMER_DATA (

(16,'5F8F72F2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(17,'C54C5779

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(18,'D1EB489E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

(19,'50035120

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(20,'F35FB6B7

Insert into CUSTOMER_DATA (CUSTO

(21,'DBA752B4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(22,'23E5896B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(23,'480ED76A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(24,'FF7A36A3

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(25,'E083DDBE

Insert into CUSTOMER_DATA (CUSTOMER

(26,'B7E4A6AD

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(27,'3BB44589

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(28,'D929DC13

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(29,'B441636D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(30,'B8D4A245

Insert into CUSTOMER_DATA (CUSTOMER_ID,S

(31,'BFD9B89D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(32,'FC1E3D87

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(33,'60473650

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(34,'F44E0BF6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(35,'77F7D210

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCA

(36,'2DE41CE6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(37,'0A4B9BF9

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(15,'F812B9FD-0EC3-30FE-3D3D

Insert into CUSTOMER_DATA (

(16,'5F8F72F2-48DF-2507-6D34

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(17,'C54C5779-7BB8-4B02-D78E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(18,'D1EB489E-3FA6-C4CC-F923

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

(19,'50035120-B48A-4904-5498

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(20,'F35FB6B7-6B68-4E9E-5D1F

Insert into CUSTOMER_DATA (CUSTO

(21,'DBA752B4-5ABE-BEB5-360C

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(22,'23E5896B-9F6C-1E18-E671

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(23,'480ED76A-D8DC-3C45-1304

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(24,'FF7A36A3-C8B4-4305-9911

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(25,'E083DDBE-282B-2CCF-7D8A

Insert into CUSTOMER_DATA (CUSTOMER

(26,'B7E4A6AD-6331-09F1-43D8

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(27,'3BB44589-FD5E-8CAB-5E76

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(28,'D929DC13-0616-A3EC-0191

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9,'B441636D-CDE4-A840-E848

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(30,'B8D4A245-67F0-9386-7AAE

Insert into CUSTOMER_DATA (CUSTOMER_ID,S

(31,'BFD9B89D-08E3-6DB9-8F76

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(32,'FC1E3D87-3955-7F7C-F865

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(33,'60473650-8AB0-12F4-9C91

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0BF6-2F46-29D4-D9BC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(35,'77F7D210-C655-21DA-B8EC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCA

(36,'2DE41CE6-4298-109C-5598

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(37,'0A4B9BF9-BA50-5B21-7CFD

ORACLE STREAM EXPLORER

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3D3D-37ECB7A5B012','Zahir','Savage','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6D34-AA2E6FD663B8','Jin','Wilcox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D78E-153879C9876A','Cha

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F923-CFB6005447F6','Chaim','Moses','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

5498-591F6F15A1FD','Lester','Fitzgerald','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5D1F-7EE852A11F8A','Otto','Dixon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

360C-572E52F02B4B','Reese','Gross','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E671-3BABD8E41E05','Griffit

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1304-EC7EECD18B25','Erasmus','Cobb','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9911-64F61FEC0CA7','Cooper','Barrett','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7D8A-0C1D77D93E5A','Stephen','Rivas','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

43D8-CA849A2FD796','Steven','Kramer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5E76-7640897ACCBE','Kirsten'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0191-8CE9B725F37F','Perry','Bender','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E848-B650635129C3','Alfonso','Velez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7AAE-11F0E30C582C','Brody','Craft','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,S

8F76-A9499A2B50D8','Trevor','Hinton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F865-EDD637D0558C','Nasim','Allis

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9C91-84B9E3B86DE7','Ali','Solomon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D9BC-4D35009C0D3B','Kennan','Schneider','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B8EC-869ED54F820D','Andrea','Santos','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCA

5598-4D74B8BEF432','Dillon','Cooke','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7CFD-82CBE0F7D9F7','Dante','Maxwell'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

37ECB7A5B012','Zahir','Savage','M');

CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

AA2E6FD663B8','Jin','Wilcox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

153879C9876A','Chadwick','Snyder','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CFB6005447F6','Chaim','Moses','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

591F6F15A1FD','Lester','Fitzgerald','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7EE852A11F8A','Otto','Dixon','M');

MER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

572E52F02B4B','Reese','Gross','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3BABD8E41E05','Griffit

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EC7EECD18B25','Erasmus','Cobb','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

64F61FEC0CA7','Cooper','Barrett','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0C1D77D93E5A','Stephen','Rivas','M');

_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CA849A2FD796','Steven','Kramer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7640897ACCBE','Kirsten'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8CE9B725F37F','Perry','Bender','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B650635129C3','Alfonso','Velez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

11F0E30C582C','Brody','Craft','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A9499A2B50D8','Trevor','Hinton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EDD637D0558C','Nasim','Allis

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

84B9E3B86DE7','Ali','Solomon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4D35009C0D3B','Kennan','Schneider','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

869ED54F820D','Andrea','Santos','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4D74B8BEF432','Dillon','Cooke','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

82CBE0F7D9F7','Dante','Maxwell'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

37ECB7A5B012','Zahir','Savage','M');

CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

AA2E6FD663B8','Jin','Wilcox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

dwick','Snyder','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CFB6005447F6','Chaim','Moses','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

591F6F15A1FD','Lester','Fitzgerald','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7EE852A11F8A','Otto','Dixon','M');

MER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

572E52F02B4B','Reese','Gross','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3BABD8E41E05','Griffith','Fields','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EC7EECD18B25','Erasmus','Cobb','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

64F61FEC0CA7','Cooper','Barrett','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0C1D77D93E5A','Stephen','Rivas','M');

_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CA849A2FD796','Steven','Kramer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7640897ACCBE','Kirsten','Malone','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8CE9B725F37F','Perry','Bender','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B650635129C3','Alfonso','Velez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

11F0E30C582C','Brody','Craft','M');

CAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A9499A2B50D8','Trevor','Hinton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EDD637D0558C','Nasim','Allison','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

84B9E3B86DE7','Ali','Solomon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4D35009C0D3B','Kennan','Schneider','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

869ED54F820D','Andrea','Santos','F');

N_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4D74B8BEF432','Dillon','Cooke','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

82CBE0F7D9F7','Dante','Maxwell','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

37ECB7A5B012','Zahir','Savage','M');

CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

AA2E6FD663B8','Jin','Wilcox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

dwick','Snyder','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CFB6005447F6','Chaim','Moses','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) valu

591F6F15A1FD','Lester','Fitzgerald','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7EE852A11F8A','Otto','Dixon','M');

MER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

572E52F02B4B','Reese','Gross','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

h','Fields','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EC7EECD18B25','Erasmus','Cobb','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

64F61FEC0CA7','Cooper','Barrett','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0C1D77D93E5A','Stephen','Rivas','M');

_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CA849A2FD796','Steven','Kramer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

,'Malone','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8CE9B725F37F','Perry','Bender','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B650635129C3','Alfonso','Velez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

11F0E30C582C','Brody','Craft','M');

CAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A9499A2B50D8','Trevor','Hinton','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

on','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

84B9E3B86DE7','Ali','Solomon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4D35009C0D3B','Kennan','Schneider','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

869ED54F820D','Andrea','Santos','F');

N_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4D74B8BEF432','Dillon','Cooke','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

,'M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

MER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

N_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

es

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

MER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

N_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Page 27: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

26

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(38,'B71A0D5D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(39,'208CB1C1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(40,'8873F976

Insert into CUSTOMER_DATA (CUSTOMER_ID,

(41,'3E4F0B65

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(42,'7DDBFAD6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(43,'1858CB09

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(44,'BF81BB5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(45,'CA2582D4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTR

(46,'0D333238

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(47,'656D6A68

Inse

(48,'7158556F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(49,'EA620BA5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(50,'DD5FF2F5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY

(51,'7DBF9ABB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(52,'F83662D2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(53,'4BA48031

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(54,'CBD8BCD1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(55,'617E92EB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,

(56,'935F0E85

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(57,'FB077D06

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(58,'E003CE4B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(59,'B645957B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(60,'A0617356

26 | INTRODUCTION TO THE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(38,'B71A0D5D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(39,'208CB1C1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(40,'8873F976

Insert into CUSTOMER_DATA (CUSTOMER_ID,

(41,'3E4F0B65

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(42,'7DDBFAD6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(43,'1858CB09

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(44,'BF81BB52

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(45,'CA2582D4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTR

(46,'0D333238

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(47,'656D6A68

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(48,'7158556F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(49,'EA620BA5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(50,'DD5FF2F5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY

(51,'7DBF9ABB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(52,'F83662D2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(53,'4BA48031

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(54,'CBD8BCD1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(55,'617E92EB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,

(56,'935F0E85

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(57,'FB077D06

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(58,'E003CE4B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(59,'B645957B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(60,'A0617356

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(38,'B71A0D5D-544E-718B-EFE1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(39,'208CB1C1-5B8D-DB0E-C434

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(40,'8873F976-89FC-24A3-71B5

Insert into CUSTOMER_DATA (CUSTOMER_ID,

(41,'3E4F0B65-7445-1CD8-8A31

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(42,'7DDBFAD6-D16B-F6A2-9F52

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(43,'1858CB09-1E4C-9272-1C10

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2-7111-B70F-78B8

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(45,'CA2582D4-40A0-C718-0F34

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTR

(46,'0D333238-D10C-8FF9-B2EB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(47,'656D6A68-7C70-02BC-32A8

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(48,'7158556F-CF03-05EA-D43F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(49,'EA620BA5-CD44-2487-87EA

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(50,'DD5FF2F5-ADE0-18F6-1E86

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY

(51,'7DBF9ABB-4979-6BE6-2BBE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(52,'F83662D2-BC5A-1E00-4910

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(53,'4BA48031-B489-21BE-3EAD

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(54,'CBD8BCD1-0385-C7F0-6D94

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(55,'617E92EB-DDA0-3200-C13C

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,

(56,'935F0E85-9F11-3B2C-25B5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(57,'FB077D06-281F-C64C-99C4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(58,'E003CE4B-6AE8-046D-E0FB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(59,'B645957B-4C28-7F54-29FB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(60,'A0617356-3C2A-32B5-FC0D

ORACLE STREAM EXPLORER

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

EFE1-A9F171ECF738','Damian','Craig','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C434-071866F99570','Baker','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

71B5-A1AB6FE9D30E','Kelly','Briggs','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,

8A31-B5BAEED5CFA7','Eric','Rice','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9F52-6320A0B9C06F','Ashton','Mack','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1C10-F46F7D49BA6F','Maxwell','Mejia','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

78B8-F83A19F58E82','Marta','Winters','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0F34-12AD26AAEB3B','Harlan','Wilcox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTR

B2EB-BCC1B1876767','Drew','Lynch','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

32A8-240E5B48332D','Felix','Cash','M');

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D43F-AF2B2DA28BEE','Clayton','Chambers','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

87EA-9CD352172BCC','Keaton','Woodward','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1E86-5E98C039D9B5','Ricardo','Ferreira','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY

2BBE-A782512D6AF5','Oliver','Hurst','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4910-53AE7FFB85C4','Channing','Hubbard','M'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3EAD-C2E245AEDC21','Peter','Frazier','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6D94-DAF8674087FA','Kieran','Farley','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C13C-CBAC5523526C','Monica','Atkins','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,

25B5-D0D8AC849427','Sharon','Wilkinson','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

99C4-46149A8555AB','Owen','Mccullough','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E0FB-9BEB6B991D33','Hayden','Wood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

29FB-7AD81627CB96','Mitsuko','Yakamoto','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FC0D-6170EE4E8D16','Garrison','Pugh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A9F171ECF738','Damian','Craig','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

071866F99570','Baker','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A1AB6FE9D30E','Kelly','Briggs','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B5BAEED5CFA7','Eric','Rice','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6320A0B9C06F','Ashton','Mack','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F46F7D49BA6F','Maxwell','Mejia','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F83A19F58E82','Marta','Winters','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

12AD26AAEB3B','Harlan','Wilcox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

BCC1B1876767','Drew','Lynch','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

240E5B48332D','Felix','Cash','M');

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

AF2B2DA28BEE','Clayton','Chambers','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9CD352172BCC','Keaton','Woodward','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5E98C039D9B5','Ricardo','Ferreira','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY

A782512D6AF5','Oliver','Hurst','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

53AE7FFB85C4','Channing','Hubbard','M'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C2E245AEDC21','Peter','Frazier','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

DAF8674087FA','Kieran','Farley','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CBAC5523526C','Monica','Atkins','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,

D0D8AC849427','Sharon','Wilkinson','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

46149A8555AB','Owen','Mccullough','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9BEB6B991D33','Hayden','Wood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7AD81627CB96','Mitsuko','Yakamoto','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6170EE4E8D16','Garrison','Pugh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A9F171ECF738','Damian','Craig','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

071866F99570','Baker','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A1AB6FE9D30E','Kelly','Briggs','F');

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B5BAEED5CFA7','Eric','Rice','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6320A0B9C06F','Ashton','Mack','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F46F7D49BA6F','Maxwell','Mejia','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F83A19F58E82','Marta','Winters','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

12AD26AAEB3B','Harlan','Wilcox','M');

Y,FIRST_NAME,LAST_NAME,GENDER) values

BCC1B1876767','Drew','Lynch','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

240E5B48332D','Felix','Cash','M');

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

AF2B2DA28BEE','Clayton','Chambers','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9CD352172BCC','Keaton','Woodward','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5E98C039D9B5','Ricardo','Ferreira','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A782512D6AF5','Oliver','Hurst','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

53AE7FFB85C4','Channing','Hubbard','M'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C2E245AEDC21','Peter','Frazier','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

DAF8674087FA','Kieran','Farley','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CBAC5523526C','Monica','Atkins','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D0D8AC849427','Sharon','Wilkinson','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

46149A8555AB','Owen','Mccullough','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9BEB6B991D33','Hayden','Wood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7AD81627CB96','Mitsuko','Yakamoto','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6170EE4E8D16','Garrison','Pugh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A9F171ECF738','Damian','Craig','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

071866F99570','Baker','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A1AB6FE9D30E','Kelly','Briggs','F');

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B5BAEED5CFA7','Eric','Rice','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F46F7D49BA6F','Maxwell','Mejia','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F83A19F58E82','Marta','Winters','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

12AD26AAEB3B','Harlan','Wilcox','M');

Y,FIRST_NAME,LAST_NAME,GENDER) values

BCC1B1876767','Drew','Lynch','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

240E5B48332D','Felix','Cash','M');

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

AF2B2DA28BEE','Clayton','Chambers','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9CD352172BCC','Keaton','Woodward','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5E98C039D9B5','Ricardo','Ferreira','M');

,FIRST_NAME,LAST_NAME,GENDER) values

A782512D6AF5','Oliver','Hurst','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

53AE7FFB85C4','Channing','Hubbard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C2E245AEDC21','Peter','Frazier','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

DAF8674087FA','Kieran','Farley','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CBAC5523526C','Monica','Atkins','F');

FIRST_NAME,LAST_NAME,GENDER) values

D0D8AC849427','Sharon','Wilkinson','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

46149A8555AB','Owen','Mccullough','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9BEB6B991D33','Hayden','Wood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7AD81627CB96','Mitsuko','Yakamoto','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6170EE4E8D16','Garrison','Pugh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Y,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Y,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

rt into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Page 28: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

27

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENT

(61,'FC53DD7B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(62,'D4256448

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(63,'5AF682E5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(64,'C86B21AA

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(65,'07AB86D5

Insert into CUSTOMER_DATA

(66,'20832A21

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(67,'EC4EFB21

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(68,'12A4FECC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

(69,'BF2D39C3

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(70,'13E4712C

Insert into CUSTOMER_DATA (CU

(71,'830373DE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(72,'0D562E79

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(73,'BE643FCC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

(74,'FD4483E9

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(75,'18555BC9

Insert into CUSTOMER_DAT

(76,'C32D18A2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(77,'EAB26AAA

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(78,'375FC126

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

(79,'45BB2865

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(80,'B4D9B9DE

Insert into CUSTOMER_DATA

(81,'B1EBF0B6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(82,'5B69FE1B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(83,'FCFC70E9

27 | INTRODUCTION TO THE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENT

(61,'FC53DD7B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(62,'D4256448

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(63,'5AF682E5

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(64,'C86B21AA

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(65,'07AB86D5

Insert into CUSTOMER_DATA

(66,'20832A21

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(67,'EC4EFB21

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(68,'12A4FECC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

(69,'BF2D39C3

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(70,'13E4712C

Insert into CUSTOMER_DATA (CU

(71,'830373DE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(72,'0D562E79

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(73,'BE643FCC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

(74,'FD4483E9

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(75,'18555BC9

Insert into CUSTOMER_DAT

(76,'C32D18A2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(77,'EAB26AAA

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(78,'375FC126

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

(79,'45BB2865

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(80,'B4D9B9DE

Insert into CUSTOMER_DATA

(81,'B1EBF0B6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(82,'5B69FE1B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(83,'FCFC70E9

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENT

(61,'FC53DD7B-393D-933D-B6B6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(62,'D4256448-B609-A58A-42B4

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(63,'5AF682E5-C189-E083-FE2D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(64,'C86B21AA-2789-A441-577A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(65,'07AB86D5-195F-85D0-2765

Insert into CUSTOMER_DATA

(66,'20832A21-D4E9-6549-973A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(67,'EC4EFB21-AFC6-A079-4FAE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(68,'12A4FECC-3BA8-1D1C-2B35

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

(69,'BF2D39C3-6030-6938-EEEA

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(70,'13E4712C-463F-BAA7-8537

Insert into CUSTOMER_DATA (CU

(71,'830373DE-038B-95C5-467C

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(72,'0D562E79-16B8-B81D-6144

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(73,'BE643FCC-5F3B-E220-3670

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

(74,'FD4483E9-928B-91C9-BDB1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(75,'18555BC9-0B99-2F86-8FBB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(76,'C32D18A2-85AA-F3A3-5FD1

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(77,'EAB26AAA-DD16-0B56-4A6B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(78,'375FC126-B176-AC8D-D31D

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

(79,'45BB2865-CAA5-29E4-3E9B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(80,'B4D9B9DE-5912-CE87-235C

Insert into CUSTOMER_DATA

(81,'B1EBF0B6-F7C1-3DC4-3214

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(82,'5B69FE1B-2B9E-3919-F988

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(83,'FCFC70E9-6334-8B6C-D538

ORACLE STREAM EXPLORER

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENT

B6B6-0AEF570E14D3','Salvador','Holman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

42B4-B5AB0C7826FE','Tyrone','Phelps','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FE2D-919DAB0EAE96','Ryan','Molina','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

577A-0A9D586011A7','Paul','Mcintosh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2765-3FEEA7D2C666','Ralph','Perez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

973A-1FD322C7C710','Jennifer','Conelly','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4FAE-9B6BE9EA62A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2B35-BB478BDAD662','George','Morse','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

EEEA-4863600E7519','Matthew','Cole','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8537-A657BEB01D28','Blake','Benson','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

467C-2573DFE51586','Debbie','Howell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6144-913CA61DA166','Na

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3670-FA99F7A1E507','Chandler','Dillon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

BDB1-A1A62FE2CA24','Hamilton','Rodriquez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8FBB-9ED4B5FBF1FC','Jeff','Mcdaniel','M');

A (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5FD1-8520D7892D40','Alfonso','Salazar','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4A6B-7BA332BCFB

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D31D-A589AA9B40EC','Maria','Nieves','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

3E9B-BE4010A3F9E2','Jonas','Sawyer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

235C-6CB165AD0FA0','Devin','Harper','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3214-35E7F5238C47','Aquila','Hatfield','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F988-10E4298CF4F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D538-7161AFA80739','Chaney','Pratt','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0AEF570E14D3','Salvador','Holman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B5AB0C7826FE','Tyrone','Phelps','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

919DAB0EAE96','Ryan','Molina','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0A9D586011A7','Paul','Mcintosh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3FEEA7D2C666','Ralph','Perez','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1FD322C7C710','Jennifer','Conelly','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9B6BE9EA62AC','Raymond','Gould','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

BB478BDAD662','George','Morse','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

4863600E7519','Matthew','Cole','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A657BEB01D28','Blake','Benson','M');

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2573DFE51586','Debbie','Howell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

913CA61DA166','Nataly','Shaffer','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FA99F7A1E507','Chandler','Dillon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

A1A62FE2CA24','Hamilton','Rodriquez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9ED4B5FBF1FC','Jeff','Mcdaniel','M');

A (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8520D7892D40','Alfonso','Salazar','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7BA332BCFB52','Calvin','Underwood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A589AA9B40EC','Maria','Nieves','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

BE4010A3F9E2','Jonas','Sawyer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6CB165AD0FA0','Devin','Harper','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

35E7F5238C47','Aquila','Hatfield','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

10E4298CF4F4','Derek','Richard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7161AFA80739','Chaney','Pratt','M');

RY,FIRST_NAME,LAST_NAME,GENDER) values

0AEF570E14D3','Salvador','Holman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B5AB0C7826FE','Tyrone','Phelps','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

919DAB0EAE96','Ryan','Molina','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0A9D586011A7','Paul','Mcintosh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3FEEA7D2C666','Ralph','Perez','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1FD322C7C710','Jennifer','Conelly','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C','Raymond','Gould','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

BB478BDAD662','George','Morse','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

4863600E7519','Matthew','Cole','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A657BEB01D28','Blake','Benson','M');

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2573DFE51586','Debbie','Howell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

taly','Shaffer','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FA99F7A1E507','Chandler','Dillon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

A1A62FE2CA24','Hamilton','Rodriquez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9ED4B5FBF1FC','Jeff','Mcdaniel','M');

A (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8520D7892D40','Alfonso','Salazar','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

52','Calvin','Underwood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A589AA9B40EC','Maria','Nieves','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

BE4010A3F9E2','Jonas','Sawyer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6CB165AD0FA0','Devin','Harper','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

35E7F5238C47','Aquila','Hatfield','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4','Derek','Richard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7161AFA80739','Chaney','Pratt','M');

RY,FIRST_NAME,LAST_NAME,GENDER) values

0AEF570E14D3','Salvador','Holman','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B5AB0C7826FE','Tyrone','Phelps','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

919DAB0EAE96','Ryan','Molina','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

0A9D586011A7','Paul','Mcintosh','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

3FEEA7D2C666','Ralph','Perez','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1FD322C7C710','Jennifer','Conelly','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

C','Raymond','Gould','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

BB478BDAD662','George','Morse','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

4863600E7519','Matthew','Cole','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A657BEB01D28','Blake','Benson','M');

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2573DFE51586','Debbie','Howell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

taly','Shaffer','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

FA99F7A1E507','Chandler','Dillon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER)

A1A62FE2CA24','Hamilton','Rodriquez','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

9ED4B5FBF1FC','Jeff','Mcdaniel','M');

A (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8520D7892D40','Alfonso','Salazar','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

52','Calvin','Underwood','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A589AA9B40EC','Maria','Nieves','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GE

BE4010A3F9E2','Jonas','Sawyer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

6CB165AD0FA0','Devin','Harper','M');

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

35E7F5238C47','Aquila','Hatfield','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4','Derek','Richard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

7161AFA80739','Chaney','Pratt','M');

RY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

RY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

NDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Page 29: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

28

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

(84,'471AF640

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(85,'332BF106

Insert into CUSTOMER_DATA (CU

(86,'6345109E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(87,'8B111703

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(88,'1C7FBB69

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(90,'BFBE734E

Insert into CUSTOMER_DATA (CUSTOMER_ID,

(91,'B577A64A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(92,'FAFCE8D2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(93,'4ED62A82

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(94,'C89BD428

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(95,'1B5C67BF

Insert into CUSTOMER_DATA (CUSTOMER_

(96,'1829958B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(97,'ECA02944

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(98,'4E8DEBDD

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(100,'47CCF80B

--------------------------------

--

--------------------------------------------------------

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(1,'TSAMP','M','Good Mor

Samples?');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

28 | INTRODUCTION TO THE

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

(84,'471AF640

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(85,'332BF106

Insert into CUSTOMER_DATA (CU

(86,'6345109E

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(87,'8B111703

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(88,'1C7FBB69

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(89,'95906C59

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(90,'BFBE734E

Insert into CUSTOMER_DATA (CUSTOMER_ID,

(91,'B577A64A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(92,'FAFCE8D2

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(93,'4ED62A82

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(94,'C89BD428

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(95,'1B5C67BF

Insert into CUSTOMER_DATA (CUSTOMER_

(96,'1829958B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(97,'ECA02944

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(98,'4E8DEBDD

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(99,'0519C7FC

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(100,'47CCF80B

--------------------------------

-- Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(1,'TSAMP','M','Good Mor

Samples?');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

INTRODUCTION TO THE ORACLE STREAM EXPLOR

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

(84,'471AF640-42B5-2D28-F057

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(85,'332BF106-9B6B-C30C-1426

Insert into CUSTOMER_DATA (CU

(86,'6345109E-26FD-198B-F11F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(87,'8B111703-04FD-D5B6-1F9B

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(88,'1C7FBB69-266F-1F8D-902A

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

89,'95906C59-DA47-5700-55C9

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(90,'BFBE734E-C80D-35C5-B441

Insert into CUSTOMER_DATA (CUSTOMER_ID,

(91,'B577A64A-C1ED-18C4-01CF

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(92,'FAFCE8D2-1500-8712-38A6

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(93,'4ED62A82-FDCE-445C-F12F

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(94,'C89BD428-8B10-933B-CC58

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(95,'1B5C67BF-33F1-9F7B-D94A

Insert into CUSTOMER_DATA (CUSTOMER_

(96,'1829958B-0CC7-0B93-B804

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(97,'ECA02944-0A5F-023B-A292

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(98,'4E8DEBDD-2E3E-B59E-D785

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

99,'0519C7FC-3292-0B00-BB97

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(100,'47CCF80B-46A0-66C8-

--------------------------------

Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(1,'TSAMP','M','Good Morning Mr. ',', would you be interested in trying out our

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

ORACLE STREAM EXPLORER

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

F057-9F3CFE2072A2','Malik','Beard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1426-349909637024','Nathaniel','Cox','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F11F-410C93E52651','Elaine','Vargas','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

1F9B-A1C5A5077241','Ni

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

902A-4F987115CABC','Hu','Stein','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

55C9-8FBE929B09FD','Pamela','Hatfield','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B441-139B7296712E','Cyrus','Gay','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,

01CF-8F72EC65FA8C','Fletcher','Wiley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

38A6-710BDE770A4A','Fitzgerald'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

F12F-335745870917','Diane','Reese','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

CC58-5B01DDA827EA','Ingrid','Harrell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D94A-11560C15617C','Kenyon','Boyer','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

B804-8F023188DC2F','Emerson','Mcgowan','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A292-223016BA2B3C','Berk','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

D785-234767DC8522','Chancellor','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

BB97-E0464F7D64ED','Grant','Williamson','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

-BA1A-2DA5FB862AEE','Judah','Salazar','M');

--------------------------------------------------------

Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

ning Mr. ',', would you be interested in trying out our

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

9F3CFE2072A2','Malik','Beard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

349909637024','Nathaniel','Cox','M');

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

410C93E52651','Elaine','Vargas','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

A1C5A5077241','Nissim','Macias','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4F987115CABC','Hu','Stein','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8FBE929B09FD','Pamela','Hatfield','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

139B7296712E','Cyrus','Gay','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8F72EC65FA8C','Fletcher','Wiley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

710BDE770A4A','Fitzgerald'

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

335745870917','Diane','Reese','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5B01DDA827EA','Ingrid','Harrell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

11560C15617C','Kenyon','Boyer','M');

ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8F023188DC2F','Emerson','Mcgowan','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

223016BA2B3C','Berk','

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

234767DC8522','Chancellor','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E0464F7D64ED','Grant','Williamson','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2DA5FB862AEE','Judah','Salazar','M');

------------------------

Loading data into the table CUSTOMER_DATA

--------------------------------------------------------

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

ning Mr. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

9F3CFE2072A2','Malik','Beard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

349909637024','Nathaniel','Cox','M');

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

410C93E52651','Elaine','Vargas','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

ssim','Macias','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4F987115CABC','Hu','Stein','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8FBE929B09FD','Pamela','Hatfield','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

139B7296712E','Cyrus','Gay','M');

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8F72EC65FA8C','Fletcher','Wiley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

710BDE770A4A','Fitzgerald','Hughes','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

335745870917','Diane','Reese','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5B01DDA827EA','Ingrid','Harrell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

11560C15617C','Kenyon','Boyer','M');

ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8F023188DC2F','Emerson','Mcgowan','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

223016BA2B3C','Berk','Simon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

234767DC8522','Chancellor','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E0464F7D64ED','Grant','Williamson','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2DA5FB862AEE','Judah','Salazar','M');

------------------------

--------------------------------------------------------

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

ning Mr. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER

9F3CFE2072A2','Malik','Beard','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

349909637024','Nathaniel','Cox','M');

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

410C93E52651','Elaine','Vargas','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

ssim','Macias','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

4F987115CABC','Hu','Stein','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8FBE929B09FD','Pamela','Hatfield','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

139B7296712E','Cyrus','Gay','M');

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8F72EC65FA8C','Fletcher','Wiley','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

,'Hughes','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

335745870917','Diane','Reese','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

5B01DDA827EA','Ingrid','Harrell','F');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

11560C15617C','Kenyon','Boyer','M');

ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

8F023188DC2F','Emerson','Mcgowan','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Simon','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

234767DC8522','Chancellor','Sears','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

E0464F7D64ED','Grant','Williamson','M');

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

2DA5FB862AEE','Judah','Salazar','M');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

ning Mr. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

ning Mr. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

STOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Insert into CUSTOMER_DATA (CUSTOMER_ID,SCAN_ENTRY,FIRST_NAME,LAST_NAME,GENDER) values

Page 30: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

29

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

Samp

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(3,'WBACK','M','Hello Mr. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM

(4,'WBACK','F','Hello Mrs. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

Insert into C

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

Listing 1

For the implementation of the eye sc

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

the person's retina reading, the latitude and the longitu

eyeScan,latitude,longitude

C86B21AA

CB281A82

0A4B9BF9

E003CE4B

B645957B

B7C841D6

0028CF68

5AF682E5

C86B21AA

1B5C67BF

Listing 2

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

instead of the example shown in

URL:

Appendix B:

Considering that Oracle Stream Explorer allows sending the

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

manner. For instance, the case study

to persons walking around the store.

a greeting action?

Thanks to the

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

others,

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

29 | INTRODUCTION TO THE

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

Samples?');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(3,'WBACK','M','Hello Mr. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM

(4,'WBACK','F','Hello Mrs. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

Insert into C

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

Listing 1. SQL script to create and populate the database tables.

For the implementation of the eye sc

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

the person's retina reading, the latitude and the longitu

eyeScan,latitude,longitude

C86B21AA-2789

CB281A82-EBF9

0A4B9BF9-BA50

E003CE4B-6AE8

B645957B-4C28

B7C841D6-DC7D

0028CF68-2264

5AF682E5-C189

C86B21AA-2789

1B5C67BF-33F1

Listing 2. Example of the CSV file used as eye scan stream.

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

instead of the example shown in

URL: http://www.ateam

Appendix B:

Considering that Oracle Stream Explorer allows sending the

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

manner. For instance, the case study

to persons walking around the store.

a greeting action?

Thanks to the Java Speech API

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

others, the first version of the Java Speech API

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

INTRODUCTION TO THE ORACLE STREAM EXPLOR

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(3,'WBACK','M','Hello Mr. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM

(4,'WBACK','F','Hello Mrs. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

SQL script to create and populate the database tables.

For the implementation of the eye sc

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

the person's retina reading, the latitude and the longitu

eyeScan,latitude,longitude

2789-A441-577A-0A9D586011A7,30.831086,

EBF9-247F-8D4C-632F2CD4DC9E,30.425764,

BA50-5B21-7CFD-82CBE0F7D9F7,30.425764,

6AE8-046D-E0FB-9BEB6B991

4C28-7F54-29FB-7AD81627CB96,

DC7D-0C32-1402-58E0E06F0C74,30.674122,

2264-D591-C3F8-EECF78E3635F,30.674122,

C189-E083-FE2D-919DAB0EAE96,

2789-A441-577A-0A9D586011A7,

33F1-9F7B-D94A-11560C15617C,30.674122,

Example of the CSV file used as eye scan stream.

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

instead of the example shown in

http://www.ateam-oracle.com/wp

Appendix B: Creating a Message

Considering that Oracle Stream Explorer allows sending the

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

manner. For instance, the case study

to persons walking around the store.

a greeting action?

Java Speech API

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

the first version of the Java Speech API

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

ORACLE STREAM EXPLORER

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(3,'WBACK','M','Hello Mr. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM

(4,'WBACK','F','Hello Mrs. ',', welcome back to the GAP.');

Insert into CUSTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

USTOM_GREETING

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

SQL script to create and populate the database tables.

For the implementation of the eye scan stream, a CSV file with sample data need to be used. The listing 2 shows an

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

the person's retina reading, the latitude and the longitu

eyeScan,latitude,longitude

0A9D586011A7,30.831086,

632F2CD4DC9E,30.425764,

82CBE0F7D9F7,30.425764,

9BEB6B991D33,

7AD81627CB96,

58E0E06F0C74,30.674122,

EECF78E3635F,30.674122,

919DAB0EAE96,

0A9D586011A7,

11560C15617C,30.674122,

Example of the CSV file used as eye scan stream.

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

instead of the example shown in listing 2. The

oracle.com/wp-content/uploads/2015/02/EyeScanStream.csv

Creating a Message-

Considering that Oracle Stream Explorer allows sending the

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

manner. For instance, the case study implemented in this paper shown

to persons walking around the store. But what if

Java Speech API, create voice

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

the first version of the Java Speech API

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(3,'WBACK','M','Hello Mr. ',', welcome back to the GAP.');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM

(4,'WBACK','F','Hello Mrs. ',', welcome back to the GAP.');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

SQL script to create and populate the database tables.

an stream, a CSV file with sample data need to be used. The listing 2 shows an

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

the person's retina reading, the latitude and the longitude from its location

0A9D586011A7,30.831086,-81.460571

632F2CD4DC9E,30.425764,-81.975556

82CBE0F7D9F7,30.425764,-81.975556

D33,-13.843414,-

7AD81627CB96,-13.843414,-

58E0E06F0C74,30.674122,-81.862946

EECF78E3635F,30.674122,-81.862946

919DAB0EAE96,-13.843414,-

0A9D586011A7,-13.843414,-

11560C15617C,30.674122,-81.862946

Example of the CSV file used as eye scan stream.

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

The complete version of the CSV file

content/uploads/2015/02/EyeScanStream.csv

-Driven Bean that Greets

Considering that Oracle Stream Explorer allows sending the output results from explorations to external systems, it

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

implemented in this paper shown

But what if the output results from the exploration were really transformed into

voice-based systems

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

the first version of the Java Speech API specification was released on October 26, 1998. From the technical

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(3,'WBACK','M','Hello Mr. ',', welcome back to the GAP.');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(4,'WBACK','F','Hello Mrs. ',', welcome back to the GAP.');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

an stream, a CSV file with sample data need to be used. The listing 2 shows an

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

de from its location.

81.460571

81.975556

81.975556

-55.371094

-55.371094

81.862946

81.862946

-55.371094

-55.371094

81.862946

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

complete version of the CSV file

content/uploads/2015/02/EyeScanStream.csv

Driven Bean that Greets

output results from explorations to external systems, it

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

implemented in this paper shown that it is p

the output results from the exploration were really transformed into

systems capable of spe

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

specification was released on October 26, 1998. From the technical

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

_MESSAGE) values

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(5,'BWELC','M','Hello Mr. ',', be welcome to our GAP Store!');

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(6,'BWELC','F','Hello Mrs. ',', be welcome to our GAP Store!');

an stream, a CSV file with sample data need to be used. The listing 2 shows an

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

complete version of the CSV file can be downloaded

content/uploads/2015/02/EyeScanStream.csv.

Driven Bean that Greets

output results from explorations to external systems, it

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

that it is possible to create custom greetings

the output results from the exploration were really transformed into

capable of speaking from text messages became

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

specification was released on October 26, 1998. From the technical

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third

(2,'TSAMP','F','Good Morning Mrs. ',', would you be interested in trying out our

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

_MESSAGE) values

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

(GREETING_ID,GREETING_CODE,GENDER,ICE_BREAK_MESSAGE,CUSTOM_MESSAGE) values

an stream, a CSV file with sample data need to be used. The listing 2 shows an

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

can be downloaded in the following

output results from explorations to external systems, it

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

ossible to create custom greetings

the output results from the exploration were really transformed into

aking from text messages became

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

specification was released on October 26, 1998. From the technical

point of view the Java Speech API is not part of the JDK implementation, it must be acquired from third-party speech

an stream, a CSV file with sample data need to be used. The listing 2 shows an

example of this file, containing only the first ten rows. The CSV file is structured having each row with three fields,

It is highly recommended that during the tests using Oracle Stream Explorer, the complete version of the file is used

the following

output results from explorations to external systems, it

would be interesting to leverage this capability to come up with solutions able to sense and respond in a contextual

ossible to create custom greetings

the output results from the exploration were really transformed into

aking from text messages became

possible. Originally developed by Sun Microsystems and in collaboration with companies like Apple, AT&T, IBM and

specification was released on October 26, 1998. From the technical

party speech

Page 31: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

30

vendors

source

To demonstrate how the FreeTTS implementation can be used to

requests via JMS, listing

greeting using a human voice

package

import

import

import

import

import

import

import

import

import

import

@MessageDriven(activationConfig = {

public

30 | INTRODUCTION TO THE

vendors that provide their own implementations. One of the most popular

source speech synthesizer written entirely in Jav

To demonstrate how the FreeTTS implementation can be used to

requests via JMS, listing

greeting using a human voice

package com.oracle.fmw.ateam.fastdata;

import javax.annotation.PostConstruct;

import javax.annotation.PreDestroy;

import javax.ejb.ActivationConfigProperty;

import javax.ejb.EJBException;

import javax.ejb.MessageDriven;

import javax.jms.MapMessage;

import javax.jms.Message;

import javax.jms.MessageListener;

import com.sun.speech.freetts.Voice;

import com.sun.speech.

@MessageDriven(activationConfig = {

@ActivationConfigProperty(

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

@ActivationConfigProperty(

propertyName = "connectionFactoryJndiName

@ActivationConfigProperty(

propertyName = "destinationJndiName", prope

public class

private

@PostConstruct

private

}

@PreDestroy

private

INTRODUCTION TO THE ORACLE STREAM EXPLOR

that provide their own implementations. One of the most popular

speech synthesizer written entirely in Jav

To demonstrate how the FreeTTS implementation can be used to

requests via JMS, listing 3 shows an example of a MDB that leverages the Java Speech API to synthesize the

greeting using a human voice.

com.oracle.fmw.ateam.fastdata;

javax.annotation.PostConstruct;

javax.annotation.PreDestroy;

javax.ejb.ActivationConfigProperty;

javax.ejb.EJBException;

javax.ejb.MessageDriven;

javax.jms.MapMessage;

javax.jms.Message;

javax.jms.MessageListener;

com.sun.speech.freetts.Voice;

com.sun.speech.freetts.VoiceManager;

@MessageDriven(activationConfig = {

@ActivationConfigProperty(

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

@ActivationConfigProperty(

propertyName = "connectionFactoryJndiName

@ActivationConfigProperty(

propertyName = "destinationJndiName", prope

GreetingListener

private Voice voice;

@PostConstruct

private void allocateVoice() {

VoiceManager voiceManager = VoiceManager.getInstance();

voice = voiceManager.getVoice("kevin16");

voice.allocate();

@PreDestroy

private void deallocateVoice() {

if (voice !=

ORACLE STREAM EXPLORER

that provide their own implementations. One of the most popular

speech synthesizer written entirely in Jav

To demonstrate how the FreeTTS implementation can be used to

3 shows an example of a MDB that leverages the Java Speech API to synthesize the

com.oracle.fmw.ateam.fastdata;

javax.annotation.PostConstruct;

javax.annotation.PreDestroy;

javax.ejb.ActivationConfigProperty;

javax.ejb.EJBException;

javax.ejb.MessageDriven;

javax.jms.MapMessage;

javax.jms.Message;

javax.jms.MessageListener;

com.sun.speech.freetts.Voice;

freetts.VoiceManager;

@MessageDriven(activationConfig = {

@ActivationConfigProperty(

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

@ActivationConfigProperty(

propertyName = "connectionFactoryJndiName

@ActivationConfigProperty(

propertyName = "destinationJndiName", prope

GreetingListener implements

Voice voice;

allocateVoice() {

VoiceManager voiceManager = VoiceManager.getInstance();

voice = voiceManager.getVoice("kevin16");

voice.allocate();

deallocateVoice() {

(voice != null) {

that provide their own implementations. One of the most popular

speech synthesizer written entirely in Java.

To demonstrate how the FreeTTS implementation can be used to

3 shows an example of a MDB that leverages the Java Speech API to synthesize the

com.oracle.fmw.ateam.fastdata;

javax.annotation.PostConstruct;

javax.ejb.ActivationConfigProperty;

freetts.VoiceManager;

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

propertyName = "connectionFactoryJndiName

propertyName = "destinationJndiName", prope

implements MessageListener {

allocateVoice() {

VoiceManager voiceManager = VoiceManager.getInstance();

voice = voiceManager.getVoice("kevin16");

deallocateVoice() {

that provide their own implementations. One of the most popular implementations is the

To demonstrate how the FreeTTS implementation can be used to create a voice

3 shows an example of a MDB that leverages the Java Speech API to synthesize the

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

propertyName = "connectionFactoryJndiName", propertyValue = "jms/connFact

propertyName = "destinationJndiName", propertyValue = "jms/greetingQueue")

MessageListener {

VoiceManager voiceManager = VoiceManager.getInstance();

voice = voiceManager.getVoice("kevin16");

implementations is the

a voice-based system that listen gre

3 shows an example of a MDB that leverages the Java Speech API to synthesize the

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

", propertyValue = "jms/connFact

rtyValue = "jms/greetingQueue")

VoiceManager voiceManager = VoiceManager.getInstance();

implementations is the FreeTTS, an open

based system that listen gre

3 shows an example of a MDB that leverages the Java Speech API to synthesize the

propertyName = "destinationType", propertyValue = "javax.jms.Queue"),

", propertyValue = "jms/connFact"),

rtyValue = "jms/greetingQueue")})

n open-

based system that listen greeting

3 shows an example of a MDB that leverages the Java Speech API to synthesize the

"),

})

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31

}

Listing 3

In the

javax.jms.MapMessage

results when the JMS

generated by the exploration, as shown in figure 24.

31 | INTRODUCTION TO THE

}

@Override

public

}

}

Listing 3. Implementation of the MDB using the Java

In the onMessage

javax.jms.MapMessage

results when the JMS

generated by the exploration, as shown in figure 24.

INTRODUCTION TO THE ORACLE STREAM EXPLOR

voice.deal

}

@Override

public void onMessage(Message message) {

if (message

MapMessage mapMessage =

String iceBreakMessage =

String customerName =

String customMessage =

String greeting =

try

}

}

}

Implementation of the MDB using the Java

onMessage()method implementation shown in listing 3, the received message is transformed into a

javax.jms.MapMessage, because this is the type of message that Oracle Stream Explorer sends the output

results when the JMS-based target is used. Also, the attributes

generated by the exploration, as shown in figure 24.

ORACLE STREAM EXPLORER

voice.deallocate();

onMessage(Message message) {

(message instanceof

MapMessage mapMessage =

String iceBreakMessage =

String customerName =

String customMessage =

String greeting =

try {

mapMessage = (MapMessage) message;

iceBreakMessage = mapMessage.getString("iceBreakMessage");

customerName = mapMessage.getString("customerName");

customMessage = mapMessage.getString("custom

greeting = iceBreakMessage + customerName + customMessage;

voice.speak(greeting);

catch (Exception ex) {

throw new

Implementation of the MDB using the Java

method implementation shown in listing 3, the received message is transformed into a

, because this is the type of message that Oracle Stream Explorer sends the output

based target is used. Also, the attributes

generated by the exploration, as shown in figure 24.

locate();

onMessage(Message message) {

instanceof MapMessage) {

MapMessage mapMessage = null

String iceBreakMessage = null

String customerName = null;

String customMessage = null

String greeting = null;

mapMessage = (MapMessage) message;

iceBreakMessage = mapMessage.getString("iceBreakMessage");

customerName = mapMessage.getString("customerName");

customMessage = mapMessage.getString("custom

greeting = iceBreakMessage + customerName + customMessage;

voice.speak(greeting);

(Exception ex) {

new EJBException(ex);

Implementation of the MDB using the Java Speech API.

method implementation shown in listing 3, the received message is transformed into a

, because this is the type of message that Oracle Stream Explorer sends the output

based target is used. Also, the attributes

generated by the exploration, as shown in figure 24.

MapMessage) {

null;

null;

;

null;

mapMessage = (MapMessage) message;

iceBreakMessage = mapMessage.getString("iceBreakMessage");

customerName = mapMessage.getString("customerName");

customMessage = mapMessage.getString("custom

greeting = iceBreakMessage + customerName + customMessage;

voice.speak(greeting);

EJBException(ex);

method implementation shown in listing 3, the received message is transformed into a

, because this is the type of message that Oracle Stream Explorer sends the output

based target is used. Also, the attributes obtained from the message matches with the ones

mapMessage = (MapMessage) message;

iceBreakMessage = mapMessage.getString("iceBreakMessage");

customerName = mapMessage.getString("customerName");

customMessage = mapMessage.getString("custom

greeting = iceBreakMessage + customerName + customMessage;

method implementation shown in listing 3, the received message is transformed into a

, because this is the type of message that Oracle Stream Explorer sends the output

from the message matches with the ones

iceBreakMessage = mapMessage.getString("iceBreakMessage");

customerName = mapMessage.getString("customerName");

customMessage = mapMessage.getString("customMessage");

greeting = iceBreakMessage + customerName + customMessage;

method implementation shown in listing 3, the received message is transformed into a

, because this is the type of message that Oracle Stream Explorer sends the output

from the message matches with the ones

iceBreakMessage = mapMessage.getString("iceBreakMessage");

greeting = iceBreakMessage + customerName + customMessage;

method implementation shown in listing 3, the received message is transformed into a

, because this is the type of message that Oracle Stream Explorer sends the output

from the message matches with the ones

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32

From the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

FreeTTS implementation also n

EAR) or

deployment are:

32 | INTRODUCTION TO THE

From the compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

FreeTTS implementation also n

EAR) or installed

deployment are:

» $FREETTS/lib/cmulex.jar

» $FREETTS/lib/

» $FREETTS/lib/en_us.jar

» $FREETTS/lib/freetts.jar

INTRODUCTION TO THE ORACLE STREAM EXPLOR

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

FreeTTS implementation also n

installed directly into the

$FREETTS/lib/cmulex.jar

$FREETTS/lib/cmu_us_kal.jar

$FREETTS/lib/en_us.jar

$FREETTS/lib/freetts.jar

ORACLE STREAM EXPLORER

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

FreeTTS implementation also need to be deployed, either toge

into the Java EE application server

cmu_us_kal.jar

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

eed to be deployed, either toge

application server classpath. The libra

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

eed to be deployed, either together with the MDB (In case of packaging it using an

classpath. The libra

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

ther with the MDB (In case of packaging it using an

classpath. The libraries that need to be considered for

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

ther with the MDB (In case of packaging it using an

ries that need to be considered for

compilation perspective, the only library from the FreeTTS implementation that needs to be available in the

classpath is the $FREETTS/lib/freetts.jar library. But from the deployment perspective, some other libraries from the

ther with the MDB (In case of packaging it using an

ries that need to be considered for

Page 34: Introduction to the Oracle Stream Explorer · Introduction to the Oracle Stream Explorer Fast Data ORACLE WHITE and Event Processing PAPER | MARCH 2015 without Software Coding

CONN E C T W I T H U S

CONN E C T W I T H U S

blogs.oracle.com/oracle

facebook.com/oracle

twitter.com/oracle

oracle.com

CONN E C T W I T H U S

blogs.oracle.com/oracle

facebook.com/oracle

twitter.com/oracle

Oracle Corporation, World Headquarters

500 Oracle Parkway

Redwood Shores, CA 94065, USA

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only,contents hereof are subject towarranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantfitness for a particulformed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by ameans, elec Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective Intel and Intel Xeon are trademarks or regiare trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo aretrademarks or registered trademarks of Adv Introduction to the Oracle Stream ExplorerMarchAuthor: Ricardo FerreiraReviewers:

Oracle Corporation, World Headquarters

500 Oracle Parkway

Redwood Shores, CA 94065, USA

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only,contents hereof are subject towarranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantfitness for a particular purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by ameans, electronic or mechanical, for any purpose, without our prior written permission.

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective

Intel and Intel Xeon are trademarks or regiare trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo aretrademarks or registered trademarks of Adv

Introduction to the Oracle Stream ExplorerMarch 2015 Author: Ricardo Ferreira Reviewers: Peter Farkas, Prabhu Thukkaram

Oracle Corporation, World Headquarters

Redwood Shores, CA 94065, USA

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only,contents hereof are subject to change without notice. This document is not warranted to be errorwarranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchant

ar purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by a

tronic or mechanical, for any purpose, without our prior written permission.

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective

Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo aretrademarks or registered trademarks of Advanced Micro Devices. UNIX is a register

Introduction to the Oracle Stream Explorer

, Prabhu Thukkaram

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only,change without notice. This document is not warranted to be error

warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantar purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are

formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by atronic or mechanical, for any purpose, without our prior written permission.

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective

stered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are

anced Micro Devices. UNIX is a register

Worldwide Inquiries

Phone: +1.650.506.7000

Fax: +1.650.506.7200

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only,change without notice. This document is not warranted to be error

warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantar purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are

formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by atronic or mechanical, for any purpose, without our prior written permission.

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective

stered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are

anced Micro Devices. UNIX is a registered trademark

Worldwide Inquiries

Phone: +1.650.506.7000

+1.650.506.7200

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only,change without notice. This document is not warranted to be error-free, nor subject to any other

warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantar purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are

formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by a

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective

stered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are

ed trademark of The Open Group.0115

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only, and the free, nor subject to any other

warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or ar purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are

formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

stered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are

of The Open Group.0115