cumulocity iot frontrunner manage iot …...2018/06/21 · data feeds streaming analytics...

© 2018 Software AG. All rights reserved. For internal use only

CUMULOCITY IOT FRONTRUNNER

MANAGE – IOT ANALYTICS

ALIM YIGITER

SR. PM CUMULOCITY IOT

2 |

YOUR DIGITAL BUSINESS PLATFORM IOT IS A SUBSET OF THE DIGITAL BUSINESS

+ HYBRID

CLOUD

ON PREMISE

ANALYTICS & DECISIONS Streaming Analytics & Artificial Intelligence In-Memory Data

PROCESS & APPLICATIONS Dynamic Process Automation

Low-Code Applications

INTEGRATION & API Hybrid Integration

API Management

DEVICES Device Connectivity

Device Management

DIGITAL ENTERPRISE

MODEL

Process Transformation &

Management

Governance, Risk &

Compliance

Portfolio Management

Enterprise Architecture

Management

INT

ER

NE

T O

F T

HIN

GS

powered by

ARIS & ALFABET

powered by

CUMULOCITY

powered by

WEBMETHODS

powered by

TERRACOTTA & APAMA

powered by

WEBMETHODS


3 |

DIFFERENT DELIVERY MODELS FOR DIFFERENT CUSTOMER NEEDS

Cumulocity IoT

Growing Maturity Level over Time

Fle

xib

ilit

y &

So

ph

isti

cati

on

IoT SaaS

Fast Entry

• Self-service, out-of-the-box

• Pre-defined solution accelerators

IoT PaaS

Fan Out (Departmental)

• High productivity, fast go-to-market

• Smart rules

• Focus on business users

IoT Framework

Scale Out (Enterprise Level)

• Cross divisional end-to-end

company processes

• Coding, professional developers

• Existing and new IoT

applications fully interwoven

© 2018 Software AG. All rights reserved.

4 |

all devices, all networks, all verticals, all use cases

…

CUMULOCITY IoT


DEVICE CONNECTIVITY

Plug & Play with existing integrations

100+ devices and gateways

300+ protocols (BACnet, KNX, Siemens..)

Connect over any IP network without VPN

Cloud Fieldbus

Central, web based end-to-end integration

LPWAN Agents

NB-IoT Model-less integration

Use 1000’s of device types/versions

Device originated data model extension

Protocols supported:

MQTT, REST, LWM2M, Tracker, SNMP

SmartREST payload compression

Device SDK’s for:

Use any environment with:

SE & ME

Integrate new device types

DEVICES

http://www.cumulocity.com/dev-center/#devices

5 |

…

CUMULOCITY IoT


ANALYTICS & DATA

DEVICES

DATA & ANALYTICS

Visualization & Data Exploration Streaming & Predictive Analytics

Apama’s patented, in-memory streaming

analytics enables:

Filtering, correlation, aggregation and

pattern detection with time and

location constraints

Enrichment of streaming data with context

data for deeper, richer analytics

Performs analytics on both discrete

events and event streams

Designed for high throughput & low

latency, with small HW footprint

Predictive analytics with R and Python

Operationalization of predictive models

Elastically scalable data store MongoDB

Complemented by Terracotta DB as

in-memory data platform

Pre-built integration with data lakes

Built-in IoT/IIoT domain data models

Data Management

Interactive business-focused mashup

dashboards with responsive design

Real-time with historic data blending

Exploratory ad-hoc & time series analysis

Supports rich set of data sources

IIoT Cockpit & Digital Twins

all devices, all networks, all verticals, all use cases


Rob Jones

Apama Product Manager

CUMULOCITY IOT FRONTRUNNER

APAMA OVERVIEW

7 |

STREAMING ANALYTICS

• What is Apama Streaming Analytics?

– Apama can apply analytics to huge volumes of fast-moving data with very low latency

• Where can it be deployed?

– Cumulocity IoT Cloud and Cumulocity IoT Edge

– Standalone - Enterprise hardware: x86 compatible hardware on Windows, & Linux

– Standalone - Other hardware: ARM embedded devices, Edge Servers

• Who is it for?

– Organizations that wish to analyze data-in-motion and drive fast decisions

• How does it help?

– Processes large numbers of data events every second and provides sub-millisecond decisioning

– Supports vast scalability, open analytics standards, very efficient use of hardware and exhibits

extreme high reliability


APAMA ELEVATOR PITCH

8 |

STREAMING ANALYTICS


THE DATA DIVIDE

BIG DATA CHASM

Global datasphere is

estimated to grow

from 16.1 ZB in 2016

to 163 ZB in 2025

80%

will contain

valuable

information

3%

prepared for

analysis

0.5%

being

analyzed

<0.5%

being

operationalized

Sources: IDC, “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things”

and "Data Age 2025 - The Evolution of Data to Life-Critical”,

1ZB (Zettabyte) = 1T GB

9 |

STREAMING ANALYTICS


PERISHABLE INSIGHTS’ VALUE FALLS RAPIDLY

Time

Business

Value

Business Event

Response Window

Response “Forrester defines perishable insights as urgent

business situations (risks and opportunities) that

firms can only detect and act on at a moment’s

notice.”

Forrester Research, Q1 2016

10 |

STREAMING ANALYTICS


DETECT PATTERNS AND ACT ON REAL-TIME INSIGHTS

Visual Alerts &

Analysis

Business

Processes

Predictive Analytics,

Business Rules, …

Data feeds Streaming Analytics Application Logic

“Software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple,

disparate live data sources and in any data format to identify simple and complex patterns to

provide applications with context to detect opportune situations, automate immediate actions, and

dynamically adapt.“ Forrester Research, March 2016

11 |

APAMA


BATCH PROCESSING COMPARED & STREAMING ANALYTICS

Batch Processing:

“What was the average

temperature of the

machine yesterday?”

Data at rest – traditional approach

• Store the data first

• Then analyze it (run queries)

Streaming Analytics Processing: “When more than 3 temperature

events are received above a threshold, alert.”

Event Streams Event Processing

Time

12 |

APAMA IN CUMULOCITY IOT

• Notifications: Send me an email if there's a power outage in one of my machines

• Remote control: Turn a device off if its temperature goes over 40 degrees

• Validation: Discard negative meter readings or meter readings that are lower

than the previous value

• Derived data: Calculate the volume of sales transactions per vending machine

per day

• Aggregation: Sum up the sales of vending machines for an organization per day

• Compression: Store location updates of all cars only once every five minutes

(but still send real-time data for the car that I am looking at to the user interface)


STREAMING ANALYTICS USE CASES

13 |


• Smart Rules allow you to select

pre-existing rules and use a

friendly UI to configure them

• A number of Smart Rules are

provided with Cumulocity

– E.g. “On geofence create alarm”

• Apama is used behind the

scenes to execute the rules


SMART RULES

14 |

APAMA IN CUMULOCITY

• “Custom CEP rules” allow you to run

specific Apama code in real-time on the

data streaming through Cumulocity

• E.g. “Create alarm if temperature too

low for 15 mins”

• Apama is used behind the scenes to

execute the rules


“CUSTOM CEP RULES”

15 |

APAMA BENEFITS

• Apama has a full

development

environment

– Code auto-

completion

– Syntax highlighting

– Interactive debugger

with breakpoints,

stepping etc.

– Connectivity with

Cumulocity for live

execution and

debugging


FIRST CLASS DEVELOPER TOOLING

16 |

APAMA BENEFITS

• Apama has extensive tooling for use with the

development environment

– Code-coverage utility

• Discover how test cases cover application code

• Identify gaps in testing

– Memory profiler

• Track how and where memory is used

– CPU profiler

• Highlight processing-intensive parts of your

application

– Data replay

• Record and replay data for simulation and testing


FIRST CLASS DEVELOPER TOOLING

17 |

APAMA BENEFITS

• Apama’s programming

language EPL is recognized

as Java-like

– Straightforward for Java

developers to get started


APAMA EPL IS JAVA-LIKE

Apama EPL:

monitor ForwardMeasurements {

action onload() {

on all Measurement(type = "c8y_TemperatureMeasurement") as m {

send Alarm(

"",

"c8y_TemperatureAlarm",

m.source,

m.time,

"Temperature measurement was created",

"ACTIVE",

"CRITICAL",

1,

new dictionary<string,any>

) to Event.CHANNEL;

}

}

}

18 |

APAMA BENEFITS

• Applications run faster in Apama and require less

memory

• Apama’s server is natively built and optimized for

performance on each platform:

– Does not require the performance overhead of working

through a JVM

– No unpredictable slow performance spikes due to Java

garbage collection

• CPU profiler tool highlights application performance

bottlenecks

– Use profiler to optimize your applications


CLASS-LEADING PERFORMANCE

19 |

APAMA BENEFITS


APAMA IN ALL IOT DELIVERY MODELS

Growing Maturity Level over Time

Fle

xib

ilit

y &

So

ph

isti

cati

on

IoT SaaS

Fast Entry

• Cloud, self-service

• Pre-defined solution accelerators

IoT PaaS

Fan Out

• High productivity, fast go-to-market

• Focus on business users

IoT Framework

Scale Out

• High flexibility & control

• Focus on IT & developers

IoT Platform

20 |

APAMA BENEFITS

• Same Apama application can be deployed across all Cumulocity IoT

environments and in standalone Apama installations (optional)


SINGLE APPLICATION WORKS IN ALL ENVIRONMENTS

Device

Thin Edge

(IoT Gateway)

Thick Edge

(On-Site Server) Regional Platform

(Cloud / On-Premise)

Global Platform

(Cloud / On-Premise)

Broad Deep

High Low

Broad Deep

High Low

Broad Deep

High Low

Broad Deep

High Low

Broad Deep

High Low

Knowledge &

Analytics

Horizon

TCO

per Device

• Fully-fledged Cumulocity IoT Platform with

embedded Apama

• Apama as part of IoT customer frameworks

• Lightweight Cumulocity

IoT Platform with

Apama

• Apama embedded in

3rd party edge

platforms

• Apama incorporating

Cumulocity IoT

device-side agent

• Apama standalone

• Apama incorporating

Cumulocity IoT device-

side agent

• Apama standalone

21 |

APAMA IN CUMULOCITY IOT V9 (AND LATER)

• Apama is the streaming analytics platform for Cumulocity IoT

– Apama is fully supported within Cumulocity IoT

• Apama and Cumulocity IoT are both Software AG products

– Direct support from the same Software AG global support team

– Integration between Apama and Cumulocity IoT provide several

benefits, which will increase over time

– Apama’s streaming analytics capabilities and tooling will

broaden to enhance what is possible today

– Apama’s roadmap is aligned with Cumulocity IoT


SOFTWARE AG STRATEGY FOR CUMULOCITY IOT

22 |


• New “Analytics Builder” available as a

Cumulocity IoT web-app

– Graphical UI for non-coders wanting to take

advantage of streaming analytics

• Quickly build and deploy analytical models

• Library of pre-built analytic blocks

• Drag and drop, web-based editor

• Manage deployment and simulation

– First release planned for “Cumulocity IoT Edge”

later in 2018


ROADMAP - ANALYTICS BUILDER

23 |



ROADMAP - ANALYTICS BUILDER

Incoming Events Computed Events

Parameters

A set of parameters

(specific to each type

of Analytic)

Encapsulated logic

Example network of four analytics

Value

Breach

Computed range

Baseline

Value Breach

Computed range

“Spike”

“Drift”

“Sum” “Filter”

24 |


• Current prototype from Apama R&D showing how a customer model could be

represented in a free-flow UI style


ROADMAP – ANALYTICS BUILDER NEW WEB UI

25 |


• Cumulocity Model

Manager

– Single click activation

of models

– Manages whether

models are running

with simulated or live

data


ROADMAP – ANALYTICS BUILDER MODEL MANAGER

26 |

APAMA ANALYTICS BUILDER

• Gate

– Blocks the input from going to output unless the gate is open and

enabled

• Direction

– Detects change in direction of the inputs. Generates direction and last

inflection point, ignoring minor variations?

• Threshold

– Compares input value against a threshold and detects above/below or

crossing the threshold

• And

– Performs a logical 'and' on the inputs

• Or

– Performs a logical 'or' on the inputs

• Measurements & Event Input

– Takes Measurement events from Cumulocity IoT and re-orders them

based on timestamp

• Measurement Output

– Outputs a Cumulocity IoT measurement

• Operation Output

– Creates operations for a specified device with pre-configured operation

name and parameters

• Delta

– Calculates the difference between successive values on a wire

• Time Delay

– Delays the input by the specified amount of time

• Difference

– Calculates the absolute difference between the connected inputs

• Latch

– Latches the latest input value received while enabled and only generates

an output if the input value changes

• Missing Data

– Generates an output if the input has not occurred for a set amount of time

• Expression

– Addition, subtraction, compare etc.

• Mean

– Mean (arithmetic average)

• Integrate

– Calculates the integral of the value over time. Integral is defined as input

value multiplied by the amount of time it maintains that value

• Toggle

– Converts two pulse inputs to a boolean output based on set and reset

signals - with optional delays


ANALYTIC BLOCKS

27 |

APAMA STANDALONE

• Apama can also be licensed for external, standalone use with Cumulocity IoT

• Additional standalone capabilities of Apama:


WHAT CAN I DO WITH STANDALONE APAMA?

– Predictive Analytics (Zementis, R)

– Direct connectivity (JMS, Kafka, MQTT, HTTP,

Universal Messaging etc.)

– Apama queries

– Custom connectivity and integration plug-ins

(C++, Java)

– Command Central for deployment, monitoring

– Command line DevOps tools

– Client APIs for custom integration

– Correlator persistence

– Dashboards

– Deployment scripts

– External in-memory stores (Terracotta)

– Database integration

– File system integration

– Web Services (SOAP)

– Log file access

– Monitoring using REST services

28 |

APAMA STANDALONE


EXAMPLE USE CASE: TEST TIME OPTIMIZATION

As-Is: Today testing equipment validates the

quality of each product at the end of assembly

leading to high efforts and costs

To-Be: Operationalize models & algorithms to

reliably predict the test result for each product

based on the correlation of different assembly

data in real-time

Universal

Messaging

Real-time Pattern Detection &

Operationalization of Predictive Models

Visualization

Alarms & Actions

Collaboration

with I4.0

29 |

STREAMING ANALYTICS

APAMA STANDALONE


PREDICTIVE ANALYTICS

MA

NA

GE

D F

ILE

TR

AN

SF

ER

DATA SCIENTISTS

ENVIRONMENT

PMML Models for

Scoring

R Scripts & models

for Post-Processing

SE

RV

ICE

GO

VE

RN

AN

CE

UNIVERSAL

MESSAGING

INTEGRATION AND

SERVICES

PMML EXECUTION

PLUGIN

Sensor Data

DASHBOARD

EVENT PROCESSING

LANGUAGE (EPL)

IN-MEMORY DATA FABRIC SERVER ARRAY

PRE-PROCESSING

CLUSTER

SCORING CLUSTER POST-PROCESSING

CLUSTER

30 |

APAMA THIN EDGE

• Apama is a native C++ application

• FAST speed

• LOW footprint

• Requires no JVM overhead

• Executes within 32MB memory

• Requires just 88MB disk space on ARM

• Perfectly suited for lightweight Edge devices


SMALL FOOTPRINT

31 |

APAMA BENEFITS


LEADER IN FORRESTER STREAMING ANALYTICS WAVE

Source: The Forrester Wave™: Streaming Analytics, Q3 2017, Forrester Research, Inc., September 7, 2017

The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of

Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is

plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not

endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available

resources. Opinions reflect judgment at the time and are subject to change.

“Software AG’s Apama continues to be a broadly

applicable and perennially capable streaming

analytics platform.”

“With its recent acquisition of Cumulocity,

Apama deeply extends its reach deeper into

industrial IoT use cases by providing device

management, digital twin, and other

connectivity-oriented services.”

“There is no stopping Apama to become the

real-time engine for digital transformation that

extends all the way from the factory floor to

direct customer interactions.”

32 |

APAMA STREAMING ANALYTICS

Market-leading streaming analytics for fast-moving data

• The most complete Streaming Analytics platform – event processing,

messaging, in-memory data management and visualization combined

• Fully integrated with Cumulocity IoT

• Supports extreme scale and performance even with applications

requiring complex, temporal and stateful analysis

• Business user and business analyst interfaces for development and

operational control

• Highest analyst rated platform in its category


SUMMARY

33 |

FREE TRIAL


CUMULOCITY IoT cumulocity.softwareag.com

35 | © 2017 Software AG. All rights reserved. For internal use only

UPCOMING LIVE WEBINARS


June 21, 2018 - MANAGE: IoT Analytics

June 28, 2018 - EXTEND: IoT Enterprise Integration

Register @ www.softwareag.com/iot_frontrunner/

Questions? Contact Us [email protected]

mailto:[email protected]

cumulocity iot frontrunner manage iot …...2018/06/21 · data feeds streaming analytics...

Documents