copyright irwin/mcgraw-hill 1998 1 data modeling introduction the presentation will address the...

Copyright Irwin/McGraw-Hill 19981

Data Modeling

Introduction

The presentation will address the following questions: What is systems modeling and what is the difference between

logical and physical system models? What is data modeling and what are its benefits? Can you recognize and understand the basic concepts and

constructs of a data model? Can you read and interpret a entity relationship data model? When in a project are data models constructed and where are they

stored? Can you discover entities and relationships? Can you construct an entity-relationship context diagram?


Data Modeling

Introduction

The presentation will address the following questions: Can you discover or invent keys for entities? Can you construct a fully attributed entity relationship diagram

and describe all data structures and attributes to the repository or encyclopedia?


Data ModelingAn Introduction to Systems

Modeling

Systems Modeling One way to structure unstructured problems is to draw models.

A model is a representation of reality. Just as a picture is worth a thousand words, most system models are pictorial representations of reality.

Models can be built for existing systems as a way to better understand those systems, or for proposed systems as a way to document business requirements or technical designs.

What are Logical Models? Logical models show what a system ‘is’ or ‘does’. They are

implementation-independent; that is, they depict the system independent of any technical implementation. As such, logical models illustrate the essence of the system.



Modeling

Systems Modeling What are Physical Models?

Physical models show not only what a system ‘is’ or ‘does’, but also how the system is physically and technically implemented. They are implementation-dependent because they reflect technology choices, and the limitations of those technology choices.

Systems analysts use logical system models to depict business requirements, and physical system models to depict technical designs.



Modeling

Systems Modeling Systems analysis activities tend to focus on the logical system

models for the following reasons: Logical models remove biases that are the result of the way the

current system is implemented or the way that any one person thinks the system might be implemented.

Logical models reduce the risk of missing business requirements because we are too preoccupied with technical details.

Logical models allow us to communicate with end-users in non-technical or less technical languages.



Modeling

Systems Modeling Data modeling is a technique for defining business requirements

for a database. Data modeling is a technique for organizing and documenting

a system’s DATA. Data modeling is sometimes called database modeling because a data model is usually implemented as a database. It is sometimes called information modeling.

Many experts consider data modeling to be the most important of the modeling techniques.

Why is data modeling considered crucial? Data is viewed as a resource to be shared by as many processes

as possible. As a result, data must be organized in a way that is flexible and adaptable to unanticipated business requirements – and that is the purpose of data modeling.



Modeling

Systems Modeling Why is data modeling considered crucial? (continued)

Data structures and properties are reasonably permanent – certainly a great deal more stable than the processes that use the data. Often the data model of a current system is nearly identical to that of the desired system.

Data models are much smaller than process and object models and can be constructed more rapidly.

The process of constructing data models helps analysts and users quickly reach consensus on business terminology and rules.


Data Modeling

CUSTOMER

Customer Number (PK) Customer Name Shipping Address Billing Address Balance Due

ORDER

Order Number (PK) Order Date Order Total Cost Customer Number (FK)

INVENTORY PRODUCT

Product Number (PK) Product Name Product Unit of Measure Product Unit Price

ORDERED PRODUCT

Ordered Product ID (PK) . Order Number (FK) . Product Number (FK) Quantity Ordered Unit Price at Time of Order

has placed

sold

sold as


Data Modeling

System Concepts for Data Modeling

System Concepts Most systems analysis techniques are strongly rooted in systems

thinking. Systems thinking is the application of formal systems theory

and concepts to systems problem solving. There are several notations for data modeling, but the actual model

is frequently called an entity relationship diagram (ERD). An ERD depicts data in terms of the entities and relationships

described by the data.


Data Modeling


Entities All systems contain data. Data describes ‘things’. A concept to abstractly represent all instances of a group of

similar ‘things’ is called an entity. An entity is something about which we want to store data.

Synonyms include entity type and entity class. An entity is a class of persons, places, objects, events, or

concepts about which we need to capture and store data. An entity instance is a single occurrence of an entity.

STUDENT

An entity


Data Modeling


Attributes The pieces of data that we want to store about each instance of a

given entity are called attributes. An attribute is a descriptive property or characteristic of an

entity. Synonyms include element, property, and field. Some attributes can be logically grouped into super-attributes

called compound attributes. A compound attribute is one that actually consists of more

primitive attributes. Synonyms in different data modeling languages are numerous: concatenated attribute, composite attribute, and data structure.

STUDENT

Name . Last Name . First Name . Middle Initial Address . Street Address . City . State or Province . Country . Postal Code Phone Number . Area Code . Exchange Number . Number Within Exchange Date of Birth Gender Race Major Grade Point Average

Attributes and compound attributes


Data Modeling


Attributes Domains:

The values for each attribute are defined in terms of three properties: data type, domain, and default.

• The data type for an attribute defines what class of data can be stored in that attribute.

• For purposes of systems analysis and business requirements definition, it is useful to declare logical (non-technical) data types for our business attributes.

• An attribute’s data type determines its domain.

– The domain of an attribute defines what values an attribute can legitimately take on.

• Every attribute should have a logical default value.

– The default value for an attribute is that value which will be recorded if not specified by the user.


Data Modeling

Logical Data Type Logical Business Meaning

NUMBER Any number, real or integer

TEXT A string of characters, inclusive of numbers. When numbers are

included in a TEXT attribute, it means we do not expect to

perform arithmetic or comparisons with those numbers.

MEMO Same as TEXT but of an indeterminate size. Some business

systems require the ability to attach potentially lengthy note to a

give database record.

DATE Any date in any format.

TIME Any time in any format.

YES/NO An attribute that can only assume one of these two values

VALUE SET A finite set of values. In most cases, a coding scheme would be

established (e.g., FR=freshman, SO=sophomore, JR=junior,

SR=senior, etc.)

IMAGE Any picture or image.


Data ModelingData Type Domain Examples

NUMBER For integers, specify the range:

{minimum - maximum}

For real numbers, specify the range and

precision:

{minimum.precision -

maximum.precision}

{10- 99}

{1.000 - 799.999}

TEXT TEXT (maximum size of attribute)

Actual values are usually infinite;

however, users may specify certain

narrative restrictions.

TEXT (30)

MEMO Not applicable. There are no restrictions

on size or content.

Not applicable.

DATE Variation on the MMDDYYYY format. To

accommodate the year 2000, do not

abbreviate year to YY. Formatting

characters are rarely stored; therefore, do

not include hyphens or slashes.

MMDDYYYY

MMYYYY

YYYY

TIME For AM/PM times: HHMMT

- or -

HHMMT

HHMM


Data Modeling

Default Value Interpretation Examples

A legal value from the

domain (as described above)

For an instance of the attribute, if the user

does not specify a value, then use this value.

0

1.00

FR

NONE or NULL For an instance of the attribute, if the user

does not specify a value, then leave it blank.

NONE

NULL

REQUIRED or NOT NULL For an instance of the attribute, require the

user to enter a legal value from the domain.

(This is used when no value in the domain is

common enough to be a default, but a some

value must be entered.)

REQUIRED

NOT NULL


Data Modeling


Attributes Identification:

An entity typically has many instances; perhaps thousands or millions and there exists a need to uniquely identify each instance based on the data value of one or more attributes.

Every entity must have an identifier or key.• An key is an attribute, or a group of attributes, which assumes a

unique value for each entity instance. It is sometimes called an identifier.

Sometimes more than one attribute is required to uniquely identify an instance of an entity.

• A group of attributes that uniquely identifies an instance of an entity is called a concatenated key. Synonyms include composite key and compound key.


Data Modeling



Frequently, an entity may have more than one key. Each of these attributes is called a candidate key.

• A candidate key is a ‘candidate to become the primary identifier’ of instances of an entity. It is sometimes called a candidate identifier. (Note: A candidate key may be a single attribute or a concatenated key.)

• A primary key is that candidate key which will most commonly be used to uniquely identify a single entity instance.

• Any candidate key that is not selected to become the primary key is called an alternate key.


Data Modeling



Sometimes, it is also necessary to identify a subset of entity instances as opposed to a single instance.

• For example, we may require a simple way to identify all male students, and all female students.

• A subsetting criteria is a attribute (or concatenated attribute) whose finite values divide all entity instances into useful subsets. Some methods call this an inversion entry.

STUDENT

Student Number (Primary Key 1) Name (Alternate Key 1) . Last Name . First Name . Middle Initial Address . Street Address . City . State or Province . Country . Postal Code Phone Number . Area Code . Exchange Number . Number Within Exchange Date of Birth Gender (Subsetting Criteria 1) Race (Subsetting Criteria 2) Major (Subsetting Criteria 3) Grade Point Average

Keys and submitting criteria


Data Modeling


Relationships Conceptually, entities and attributes do not exist in isolation. Entities interact with, and impact one another via relationships to

support the business mission. A relationship is a natural business association that exists

between one or more entities. The relationship may represent an event that links the entities, or merely a logical affinity that exists between the entities.

A connecting line between two entities on an ERD represents a relationship.

A verb phrase describes the relationship.• All relationships are implicitly bidirectional, meaning that they

can interpreted in both directions.


Data Modeling

STUDENT CURRICULUMis enrolled inis being studied by


Data Modeling


Relationships Cardinality:

Each relationship on an ERD also depicts the complexity or degree of each relationship and this is called cardinality.

• Cardinality defines the minimum and maximum number of occurrences of one entity for a single occurrence of the related entity. Because all relationships are bi-directional, cardinality must be defined in both directions for every relationship.


Data Modeling

C a r d i n a l i t yI n t e r p r e t a t i o n

M i n i m u mI n s t a n c e s

M a x i m u mI n s t a n c e s

G r a p h i c N o t a t i o n

E x a c t l y o n e 1 1

Z e r o o r o n e 0 1

O n e o r m o r e 1 m a n y ( > 1 )

Z e r o , o n e , o r m o r e 0 m a n y ( > 1 )

M o r e t h a n o n e > 1 > 1

F i g u r e 5 . 3


Data Modeling


Relationships Degree:

The degree of a relationship is the number of entities that participate in the relationship.

• A binary relationship has a degree = 2, because two different entities participated in the relationship.

Relationships may also exist between different instances of the same entity.

• This is called a recursive relationship (sometimes called a unary relationship; degree = 1).


Data Modeling

COURSE

Course Id (Primary Key) . Subject Abbreviation . Course Number Course Title Course Credit

is a prerequisite for

has as a prerequisite


Data Modeling


Relationships Degree: (continued)

Relationships can also exist between more than two different entities.

• These are sometimes called N-ary relationships.

• A relationship existing among three entities is called a 3-ary or ternary relationship.

• An N-ary relationship maybe associated with an associative entity.

– An associative entity is an entity that inherits its primary key from more than one other entity (parents). Each part of that concatenated key points to one and only one instance of each of the connecting entities.


Data Modeling COURSE

Course ID (Primary Key) . Subject Abbreviation . Course Number Course Title Credit

INSTRUCTOR

Instructor ID Code (Primary Key) Instructor Name . Last Name . First Name . Middle Initial

ROOM

Classroom ID . Building Abbreviation . Room Number Number of Seats

SCHEDULED CLASS

Scheduled Class ID (Primary Key) . Course ID . Instructor ID . Room ID Division Number Days of Week Start Time End Time

meets as is assigned to

is assigned to


Data Modeling


Relationships Foreign Keys:

A relationship implies that instances of one entity are related to instances of another entity.

To be able to identify those instances for any given entity, the primary key of one entity must be migrated into the other entity as a foreign key.

• A foreign key is a primary key of one entity that is contributed to (duplicated in) another entity for the purpose of identifying instances of a relationship. A foreign key (always in a child entity) always matches the primary key (in a parent entity).


Data Modeling

CURRICULUM Program of Study Code (Primary Key) Title of Program Type of Degree Awarded (Subsetting Criteria 1) Department Number (Foreign Key)

DEPARTMENT Department Number (Primary Key) Department Name

offers is offered by


Data Modeling


Relationships Foreign Keys: (continued)

When you have a relationship that you cannot differentiate between parent and child it is called a non-specific relationship.

• A non-specific relationship (or many-to-many relationship) is one in which many instances of one entity are associated with many instances of another entity. Such relationships are suitable only for preliminary data models, and should be resolved as quickly as possible.

• All non-specific relationships can be resolved into a pair of one-to-many relationships by inserting an associative entity between the two original entities.


Data Modeling STUDENT

Student Number (Primary Key 1) Name (Alternate Key 1) . Last Name . First Name . Middle Initial Address . Street Address . City . State or Province . Country . Postal Code Phone Number . Area Code . Exchange Number . Number Within Exchange Date of Birth Gender (Subsetting Criteria 1) Race (Subsetting Criteria 2) Grade Point Average

FIGURE(a)

CURRICULUM

Program of Study Code (Primary Key) Title of Program Type of Degree Awarded (Subsetting Criteria 1)

applies to is enrolled in

STUDENT

Student Number (Primary Key 1) Name (Alternate Key 1) . Last Name . First Name . Middle Initial Address . Street Address . City . State or Province . Country . Postal Code Phone Number . Area Code . Exchange Number . Number Within Exchange Date of Birth Gender (Subsetting Criteria 1) Race (Subsetting Criteria 2) Grade Point Average

CURRICULUM Program of Study Code (Primary Key) Title of Program Type of Degree Awarded (Subsetting Criteria 1)

has declared

is being studied by

FIGURE (b)

MAJOR

Major ID (Primary Key) . Student Number (Foreign Key) . Program of Study Code (Foriegn Key) Date Enrolled Current Candidate for Degree?


Data Modeling


Relationships Generalization:

Generalization is an approach that seeks to discover and exploit the commonalties between entities.

• Generalization is a technique wherein the attributes that are common to several types of an entity are grouped into their own entity, called a supertype.

• An entity supertype is an entity whose instances store attributes that are common to one or more entity subtypes.

– The entity supertype will have one or more one-to-one relationships to entity subtypes. These relationships are sometimes called IS A relationships (or WAS A, or COULD BE A) because each instance of the supertype ‘is also an’ instance of one or more subtypes.


Data Modeling


Relationships Generalization: (continued)

• An entity subtype is an entity whose instances inherit some common attributes from an entity supertype, and then add other attributes that are unique to an instances of the subtype.

An entity can be both a supertype and subtype. Through inheritance, the concept of generalization in data

models permits the the reduction of the number of attributes through the careful sharing of common attributes.

• The subtypes not only inherit the attributes, but also the data types, domains, and defaults of those attributes.

• In addition to inheriting attributes, subtypes also inherit relationships to other entities.


Data Modeling PERSON Personal ID Number (Primary Key) Name . Last Name . First Name . Middle Initial Gender (Subsetting Criteria 1) Race (Subsetting Criteria 2) Marital Status (Subsetting Criteria 3)

STUDENT

Personal ID Number = Student Number (Primary Key) all attributes from PERSON

EMPLOYEE

Personal ID Number = Social Security Number (Primary Key) all attributes from PERSON plus Pension Plan Code Life Insurance Plan Code Medical Insurance Plan Code Vacation Days Accumulated Sick Days Acculumlated

ADDRESS

is a is a

can be contacted

at

PROSPECT

all attributes from PERSON and STUDENT plus First Contact Date Last Contact Date Has Visited Campus?

ALUMNUS

all attributes from PERSON and STUDENT plus Member of Alumni Association? Job in Field of Study? Last Known Salary

FORMER STUDENT

all attributes from PERSON and STUDENT plus Reason for Withdrawal Plans to Return?

CURRENT STUDENT

all attributes from PERSON and STUDENT plus Number of Credits Earned Grade Point Average Encumberance Status Financial Aid Eligibility Status

is a

is a

could be a

could be a

CONTRACTis bound by

AWARDED DEGREE

has earned


Data ModelingThe Process of Logical Data

Modeling

Strategic Data Modeling Many organizations select application development projects based

on strategic information system plans. Strategic planning is a separate project.

This project produces an information systems strategy plan that defines an overall vision and architecture for information systems.

• Almost always, the architecture includes an enterprise data model.



Modeling

Strategic Data Modeling An enterprise data model typically identifies only the most

fundamental of entities. The entities are typically defined (as in a dictionary) but they

are not described in terms of keys or attributes. The enterprise data model may or may not include relationships

(depending on the planning methodology’s standards and the level of detail desired by executive management). If relationships are included, many of them will be non-

specific. The enterprise data model is usually stored in a corporate

repository.



Modeling

Data Modeling During Systems Analysis The data model for a single system or application is usually called

an application data model. Logical data models have a DATA focus and a SYSTEM USER

perspective. Logical data models are typically constructed as deliverables of

the study and definition phases of a project. Logical data models are not concerned with implementation

details or technology, they may be constructed (through reverse engineering) from existing databases.

Data models are rarely constructed during the survey phase of systems analysis.


Data ModelingINFORMATION SYSTEMS FRAMEWORK

SYSTEM

ANALYSTS

SYSTEMBUILDERS

(components)

SYSTEMDESIGNERS

(specification)

SYSTEMUSERS

(requirements)

SYSTEMOWNERS

(scope)

ExistingDatabases

andTechnology

Data Requirements

data models

Business Subjects

entities and definitions

FOCUS ONSYSTEM

DATA

FOCUS ONSYSTEM

PROCESSES

FOCUS ON SYSTEM

INTERFACES

ExistingApplications

andTechnology

ExistingInterfaces

andTechnology

ExistingNetworks

andTechnology

FOCUS ONSYSTEM

GEOGRAPHY

Definition Phase

(establish and

prioritize

business system

requirements)

Study Phase

(establish

system

improvement

objectives)

Survey Phase

(establish scope

and project plan)

FASTMethodology

CUSTOMER customer-no customer-name customer-rating balance-due

PRODUCT product-no product-name unit-of-measure unit-price quantity-available

ORDER order-no order-date products-ordered quantities-ordered

Customers order zero, one, or more products. Products may be ordered by zero, one, or more customers.

Reverse

Engineering

(optional)



Modeling

Data Modeling During Systems Analysis Data modeling is rarely associated with the study phase of systems

analysis. Most analysts prefer to draw process models to document the current system. Many analysts report that data models are far superior for the

following reasons: • Data models help analysts to quickly identify business vocabulary

more completely than process models.

• Data models are almost always built more quickly than process models.

• A complete data model can be fit on a single sheet of paper. Process models often require dozens of sheets of paper.

• Process modelers too easily get hung up on unnecessary detail.



Modeling

Data Modeling During Systems Analysis Many analysts report that data models are far superior for the

following reasons: (continued)• Data models for existing and proposed systems are far more

similar than process models for existing and proposed systems. Consequently, there is less work to throw away as you move into later phases.

A study phase model should include only entities relationships, but no attributes – a context data model. The intent is to refine the understanding of scope; not to get

into details about the entities and business rules.



Modeling

Data Modeling During Systems Analysis The definition phase data model will be constructed in at least two

stages: 1 A key-based data model will be drawn.

• This model will eliminate non-specific relationships, add associative entities, include primary, alternate keys, and foreign keys, plus precise cardinalities and any generalization hierarchies.

2 A fully attributed data model will be constructed. • The fully attributed model includes all remaining descriptive

attributes and subsetting criteria.

– Each attribute is defined in the repository with data types, domains, and defaults.

The completed data model represents all of the business requirements for a system’s database.



Modeling

Looking Ahead to Systems Configuration and Design The logical data model from systems analysis describes business

data requirements, not technical solutions. The purpose of the configuration phase is to determine the best

way to implement those requirements with database technology. During system design, the logical data model will be transformed

into a physical data model (called a database schema) for the chosen database management system. This model will reflect the technical capabilities and limitations

of that database technology, as well as the performance tuning requirements suggested by the database administrator.

The physical data model will also be analyzed for adaptability and flexibility through a process called normalization.



Modeling

Fact-Finding and Information Gathering for Data Modeling

Data models cannot be constructed without appropriate facts and information as supplied by the user community. These facts can be collected by a number of techniques such as

sampling of existing forms and files; research of similar systems; surveys of users and management; and interviews of users and management.

The fastest method of collecting facts and information, and simultaneously constructing and verifying the data models is Joint Application Development (JAD).


Data ModelingPurpose Candidate Questions

Discover the systementities

What are the subjects of the business? In other words, whattypes of persons, organizations, organizational units, places,things, materials, or events are used in, or interact with thissystem, about which data must be captured or maintained?How many instances of each subject exist?

Discover the entity keys What unique characteristic (or characteristics) distinguishes aninstance of each subject from other instances of the samesubject? Are there any plans to change this identificationscheme in the future?

Discover entity subsettingcriteria

Are there any characteristics of a subject that divide allinstances of the subject into useful subsets? Are there anysubsets of the above subjects for which you have no convenientway to group instances?

Discover attributes anddomains

What characteristics describe each subject? For each of thesecharacteristics: (1) what type of data is stored? (2) who isresponsible for defining legitimate values for the data? (3) whatare the legitimate values for the data? (4) is a value required?and (5) is there any default value that should be assigned if youdon’t specify otherwise?

Discover security andcontrol needs

Are there any restrictions on who can see or use the data? Whois allowed to create the data? Who is allowed to update thedata? Who is allowed to delete the data?

Discover data timingneeds

How often does the data change? Over what period of time isthe data of value to the business? How long should we keep thedata? Do you need historical data or trends? If a characteristicchanges, must you know the former values?

Discover generalizationhierarchies

Are all instances of each subject the same? That is, are therespecial types of each subject that are described or handleddifferently? Can any of the data be consolidated for sharing?

Discover relationshipsand degrees

What events occur that imply associations between subjects?What business activities or transactions require involvehandling or changing data about several different subjects of thesame or a different type?

Discover cardinalities Is each business activity or event handled the same way or arethere special circumstances? Can an event occur with onlysome of the associated subjects, or must all the subjects beinvolved?



Modeling

Computer-Aided Systems Engineering (CASE) for Data Modeling

Data models are stored in the repository. In a sense, the data model is metadata – that is, data about the

business’ data. Computer-aided systems engineering (CASE) technology,

provides the repository for storing the data model and its detailed descriptions.



Modeling


Using a CASE product, you can easily create professional, readable data models without the use of paper, pencil, erasers, and templates. The models can be easily modified to reflect corrections and

changes suggested by end-users. Most CASE products provide powerful analytical tools that can

check your models for mechanical errors, completeness, and consistency.



Modeling


Not all data model conventions are supported by all CASE products. It is very likely that any given CASE product may force the

company to adapt their methodology’s data modeling symbols or approach so that it is workable within the limitations of their CASE tool.


Data Modeling

How to Construct Data Models

1st Step - Entity Discovery The first task in data modeling is to discover those fundamental

entities in the system that are or might be described by data. There are several techniques that may be used to identify entities.

During interviews or JAD sessions with system owners and users, pay attention to key words in their discussion.

During interviews or JAD sessions, specifically ask the system owners and users to identify things about which they would like to capture, store, and produce information.

Study existing forms and files. Some CASE tools can reverse engineer existing files and

databases into physical data models.


Data Modeling


1st Step - Entity Discovery A true entity has multiple instances—dozens, hundreds, thousands,

or more! Entities should be named with nouns that describe the person,

event, place, or tangible thing about which we want to store data. Try not to abbreviate or use acronyms. Names should be singular so as to distinguish the logical

concept of the entity from the actual instances of the entity. Define each entity in business terms.

Don’t define the entity in technical terms, and don’t define it as ‘data about …’.

Your entity names and definitions should establish an initial glossary of business terminology that will serve both you and future analysts and users for years to come.


Data ModelingEntity Name Business Definition

AGREEMENT A contract whereby a member agrees to purchase a certain number ofproducts within a certain time. After fulfilling that agreement, themember becomes eligible for bonus credits that are redeemable for freeor discounted products.Note: A major system improvement objective is to make agreementsmore flexible with respect to other clubs. Currently, only purchaseswithin the club that issued an agreement count toward credits. Anothersystem improvement objective would award bonus credits for eachpurchase leading up to fulfillment of the agreement, with acceleratedbonuses after fulfillment of the agreement.

CLUB A SoundStage membership group to which members can belong. Clubstend to be organized according to product interests such as music versusmovies versus games; or specialized media interests such as DigitalVideo Disks (DVD) or Nintendo.Note: Cross-club interaction is a desired objective for the new system.

MEMBER An active member of one or more clubs.Note: A target system objective is to re-enroll inactive members asopposed to deleting them.

MEMBER ORDER An order generated for a member as part of a monthly promotion, or anorder initiated by a member.Note: The current system only supports orders generated frompromotions; however, customer initiated orders have been given a highpriority as an added option in the proposed system.

PRODUCT An inventoried product available for promotion and sale to members.Note: System improvement objectives include (1) compatibility withnew bar code system being developed for the warehouse, and (2)adaptability to a rapidly changing mix of products.

PROMOTION A monthly or quarterly event whereby dated orders are generated for allmembers in a club. Members then have some period of time to cancel oraccelerate fulfillment of that order, after which the order is automaticallyfilled.


Data Modeling


2nd Step - The Context Data Model The second task in data modeling is to construct the context data

model. The context data model includes the fundamental or

independent entities that were previously discovered.• An independent entity is one which exists regardless of the

existence of any other entity. Its primary key contain no attributes that would make it dependent on the existence of another entity.

• Independent entities are almost always the first entities discovered in your conversations with the users.

Relationships should be named with verb phrases that, when combined with the entity names, form simple business sentences or assertions.

• Always name the relationship from parent-to-child.


Data Modeling

MEMBER ORDERComment

PRODUCTComment

MEMBERComment

PROMOTIONComment

AGREEMENTComment

CLUBComment

responds to

is featured in

places

establishessponsors

belongs to

sells binds

generates


Data Modeling


3rd Step - The Key-Based Data Model The third task is to identify the keys of each entity. The following guidelines are suggested for keys:

The value of a key should not change over the lifetime of each entity instance.

The value of a key cannot be null. Controls must be installed to ensure that the value of a key is

valid.


Data Modeling


3rd Step - The Key-Based Data Model The following guidelines are suggested for keys: (continued)

Some experts suggest that you avoid intelligent keys because the key may change over the lifetime of the entity instance.

• An intelligent key is a business code whose structure communicates data about an entity instance (such as its classification, size, or other properties).

• A code is a group of characters and/or digits that identifies and describes something in the business system.

Other experts suggest that you avoid intelligent keys because business codes can return value to the organization because they can be quickly processed by humans without the assistance of a computer.


Data Modeling


3rd Step - The Key-Based Data Model The following guidelines are suggested for keys: (continued)

Consider inventing a surrogate key instead to substitute for large concatenated keys of independent entities.

• This suggestion is not practical for associative entities since because each part of the concatenated key is a foreign key that must precisely match its parent entity’s primary key.

If you cannot define keys for an entity, it may be that the entity doesn’t really exist—that is, multiple occurrences of the so-called entity do not exist.


Data Modeling


3rd Step - The Key-Based Data Model Business Codes

There are several types of codes and they can be combined to form effective means for entity instance identification.

• Serial codes assign sequentially generated numbers to entity instances.

– Many database management systems can generate and constrain serial codes to a business’ requirements.

• Block codes are similar to serial codes except that serial numbers are divided into groups that have some business meaning.

• Alphabetic codes use finite combinations of letters (and possibly numbers) to describe entity instances.

– Alphabetic codes must usually be combined with serial or block codes in order to uniquely identify instances of most entities.


Data Modeling



There are several types of codes and they can be combined to form effective means for entity instance identification. (continued)

• In significant position codes, each digit or group of digits describes a measurable or identifiable characteristic of the entity instance.

– Significant digit codes are frequently used to code inventory items.

• Hierarchical codes provide a top-down interpretation for an entity instance.

– Every item coded is factored into groups, subgroups, and so forth.


Data Modeling



The following guidelines are suggested when creating a business coding scheme:

• Codes should be expandable to accommodate growth.

• The full code must result in a unique value for each entity instance.

• Codes should be large enough to describe the distinguishing characteristics, but small enough to be interpreted by people without a computer.

• Codes should be convenient. A new instance should be easy to create.


Data Modeling

PRODUCT ON ORDERKey Data

Order-Number [PK1] [FK]Member-Number [PK2] [FK]Product-Number [PK3] [FK]Universal-Product-Code [PK4] [FK]

PROMOTIONKey Data

Product-Number [PK2] [FK]Club-Name [PK1] [FK]Universal-Product-Code [PK3] [FK]

PRODUCTKey Data

Product-Number [PK1]Universal-Product-Code [PK2]

MEMBER ORDERKey Data

Order-Number [PK1]Member-Number [PK2] [FK]

AGREEMENTKey Data

Club-Name [PK2] [FK]Agreement-Number [PK1]

CLUB MEMBERSHIPKey Data

Member-Number [PK2] [FK]Club-Name [PK3] [FK]

MEMBERKey Data

Member-Number [PK1]

CLUBKey Data

Club-Name [PK1]

sold as

sells

enrolls in

sponsors

binds

responds to

is featured in

places

establishessponsors

generates


Data Modeling


4th Step - Generalized Hierarchies At this time, it would be useful to identify any generalization

hierarchies in a business problem.


Data Modeling

PRODUCT ON AN ORDERKey Data

Order-Number [PK1] [FK]Product-Number [PK2] [FK]Universal-Product-Code [PK3] [FK]


Club-Name [PK1] [FK]Member-Number [PK2] [FK]Agreement-Number [PK3] [FK]

TITLEKey Data

Product-Number [PK1] [FK]Universal-Product-Code [PK2] [FK]

MERCHANDISEKey Data


VIDEO TITLEKey Data


GAME TITLEKey Data


AUDIO TITLEKey Data


PRODUCTKey Data

Product-Number [PK1]Universal-Product-Code [PK2]

AGREEMENTKey Data

Club-Name [PK2] [FK]Agreement-Number [PK1]

MEMBERKey Data

Member-Number [PK1]


Order-Number [PK1]

PROMOTIONKey Data

Club-Name [PK1] [FK] CLUBKey Data

Club-Name [PK1]

generates

sold as

sells

responds to

placed

sponsors

sponsors

binds

establishes

enrolls in

generates

is a

is a


Data Modeling


5th Step - The Fully Attributed Data Model The fifth task is to identify the remaining data attributes.

The following guidelines are offered for attribution.• Many organizations have naming standards and approved

abbreviations.

– The data or repository administrator usually maintains such standards.

• Many attributes share common base names such as NAME, ADDRESS, DATE.

– Unless the attributes can be generalized into a supertype, it is best to give each variation a unique name such as:

CUSTOMER NAME vs SUPPLIER NAME

– Names must be distinguishable across projects.

• Logical attribute names should not be abbreviated.


Data Modeling


5th Step - The Fully Attributed Data Model The following guidelines are offered for attribution.

(continued)• For attributes that have only YES or NO values, name as

questions.

– For example, CANDIDATE FOR A DEGREE?

• Each attribute should be mapped to only one entity.

– Foreign keys are the exception – they identify associated instances of related entities.

• An attribute’s domain should not be based on logic.


Data ModelingMEMBERKey Data

Member-Number [PK1]Non-Key Data

Member-Name. Last-Name. First-Name. Middle-InitialMember-StatusMember-Street-AddressMember-Post-Office-BoxMember-CityMember-StateMember-Zip-CodeMember-Daytime-Phone-Number. Area-Code. Phone-Number. Extension ()Member-Date-of-Last-OrderMember-BalanceMember-Credit-Card-TypeMember-Credit-Card-NumberMember-Credit-Card-Expire-DateMember-Bonus-Balance


Order-Number [PK1]Non-Key Data

Order-Creation-DateOrder-Fill-DateShipping-Address-NameShipping-Street-AddressShipping-CityShipping-StateShipping-ZipShipping-InstructionsOrder-Sub-TotalOrder-Sales-TaxOrder-Shipping-MethodOrder-Shipping-&-Handling-CostOrder-StatusOrder-Prepaid-AmountOrder-Prepayment-MethodMember-Number [FK]Club-Name [FK]Promotion-Number

VIDEO TITLEKey Data

Product-Number [PK1] [FK]Universal-Product-Code [PK2] [FK]Non-Key Data

ProducerDirectorVideo-CategoryVideo-Sub-CategoryClosed-CaptionedLanguageRunning-TimeVideo-Media-TypeVideo-EncodingScreen-AspectMPA-Rating-Code

GAME TITLEKey Data


ManufacturerGame-CategoryGame-Sub-CategoryGame-PlatformGame-Media-TypeNumber-of-PlayersParent-Advisory-Code

PRODUCTKey Data

Product-Number [PK1]Universal-Product-Code [PK2]Non-Key Data

Product-Quantity-in-StockProduct-TypeManf-Suggested-PriceClub-Default-PriceSpecial-PriceUnits-Sold-Month-to-DateUnits-Sold-Year-to-DateUnits-Sold-Lifetime

PRODUCT ON AN ORDERKey Data

Order-Number [PK1] [FK]Product-Number [PK2] [FK]Universal-Product-Code [PK3] [FK]

Non-Key DataQuantity-OrderedQuantity-ShippedQuantity-BackorderedPurchase-Unit-PriceCredits-Earned

AUDIO TITLEKey Data


ArtistAudio-CategoryAudio-Sub-CategoryNumber-of-Units-in-PackageAudio-Media-CodeContent-Advisory-Code

TITLEKey Data


Title-of-WorkTitle-CoverCatalog-DescriptionCopyright-DateEntertainment-CategoryCredit-Value

PROMOTIONKey Data

Club-Name [PK1] [FK]Non-Key Data

Promotion-NumberPromotion-Release-DatePromotion-StatusPromotion-TypeAutomatic-Fill-DelayProduct-Number [FK]Universal-Product-Code [FK]

MERCHANDISEKey Data


Merchandise-NameMerchandise-DescriptionMerchadise-TypeUnit-of-Measure


Club-Name [PK1] [FK]Member-Number [PK2] [FK]Agreement-Number [PK3] [FK]

Non-Key DataDate-EnrolledExpiration-DateNumber-of-Credits-RequiredNumber-of-Credits-Earned

AGREEMENTKey Data

Club-Name [PK2] [FK]Agreement-Number [PK1]Non-Key Data

Agreement-Active-DateAgreement-Expire-DateFulfillment-PeriodRequired-Number-of-Credits

CLUBKey Data

Club-Name [PK1]Non-Key Data

Club-DescriptionClub-Charter-Date

sold as

sells

responds to

placed

sponsors

sponsors

binds

establishes

enrolls in

generates

generates

is a

is a


Data Modeling


6th Step - The Fully Described Model The last task is to fully describe the data model.

This task is the most time consuming. This task can be started in parallel with the key-based model or

fully attributed model, but it is usually the last data modeling task completed.

At this time the descriptions for the attributes are still incomplete – they require domains.

• Most CASE tools provide extensive facilities for describing the data types, domains, and defaults for all attributes to the repository.


Data Modeling


6th Step - The Fully Described Model Additional descriptive properties may be recorded for attributes

such as:• Who should be able to create, delete, update, and access each

attribute?

• How long should each attribute (or entity) be kept before the data is deleted or archived?


Data Modeling

The Next Generation

Data modeling should remain a value-added skill for many years.

The demand for data modeling as a skill is dependent on two factors: (1) the need for databases, and (2) the use of relational database management system

technology to implement those databases. • There is some belief that relational database technology will

eventually be replaced by object technology.

• If that were to happen, data modeling would be replaced by object modeling techniques.

• Even as object database technology becomes available, we expect the relational database industry to add object features and technologies to their product lines.


Data Modeling

The Next Generation

CASE technology will continue to improve. Today’s better CASE tools provide a two-way synchronization

between the logical data models and their database designs. This synchronization will likely extend as CASE vendors enable

their tools to directly communicate and interoperate with database management systems and working databases.


Data Modeling

Summary

Introduction An Introduction to Systems Modeling System Concepts for Data Modeling The Process of Logical Data Modeling How to Construct Data Models The Next Generation

copyright irwin/mcgraw-hill 1998 1 data modeling introduction the presentation will address the...

Documents

data models

systems data

data modeling introduction

logical models

logical system models

database modeling

information modeling

physical system models