Systems analysis and design methods 7th whitten and benley chapter 05

Data ModelingAn Introduction to Systems Modeling models for the following reasons: current system is implemented or the way that any one person thinks the system might be implemented..

Data Modeling

Introduction

logical and physical system models?

constructs of a data model?

stored?

Data Modeling

Introduction

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

Data Modeling

An Introduction to Systems

Modeling

a thousand words, most system models are pictorial representations of reality

understand those systems, or for proposed systems as a way to document business requirements or technical designs

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

models illustrate the essence of the system.

Data Modeling

An Introduction to Systems

Modeling

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

requirements, and physical system models to depict technical designs

Data Modeling

An Introduction to Systems

Modeling

models for the following reasons:

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

requirements because we are too preoccupied with technical details

non-technical or less technical languages

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.

the modeling techniques

as possible As a result, data must be organized in a way that is flexible and adaptable to unanticipated business requirements

Data Modeling

An Introduction to Systems

Modeling

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

and can be constructed more rapidly

users quickly reach consensus on business terminology and rules

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

and concepts to systems problem solving.

is frequently called an entity relationship diagram (ERD).

described by the data

Data Modeling

System Concepts for Data Modeling

‘things’ is called an entity

Synonyms include entity type and entity class.

concepts about which we need to capture and store data

STUDENT

An entity

Data Modeling

System Concepts for Data Modeling

given entity are called attributes

entity Synonyms include element, property, and field.

called compound attributes

primitive attributes Synonyms in different data modeling

languages are numerous: concatenated attribute, composite

attribute, and data structure.

Data Modeling

System Concepts for Data Modeling

properties: data type, domain, and default

stored in that attribute.

definition, it is useful to declare logical (non-technical) data types for our business attributes

can legitimately take on

Data Modeling

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.)

Data Modeling

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

or

-HHMMT

Data Modeling

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

System Concepts for Data Modeling

millions and there exists a need to uniquely identify each instance based on the data value of one or more attributes

unique value for each entity instance It is sometimes called an

identifier.

identify an instance of an entity

entity is called a concatenated key Synonyms include composite

Data Modeling

System Concepts for Data Modeling

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.)

be used to uniquely identify a single entity instance.

is called an alternate key.

Data Modeling

System Concepts for Data Modeling

instances as opposed to a single instance

students, and all female students.

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)

Gender (Subsetting Criteria 1)

Race (Subsetting Criteria 2)

Major (Subsetting Criteria 3)

Grade Point Average

Keys and submitting criteria

Data Modeling

System Concepts for Data Modeling

support the business mission

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

relationship

interpreted in both directions

Data Modeling

STUDENT is being studied by is enrolled in CURRICULUM

Data Modeling

System Concepts for Data Modeling

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

Cardinality

Interpretation

Minimum Instances

Maximum Instances

Graphic Notation

Figure 5.3

Data Modeling

System Concepts for Data Modeling

participate in the relationship

entities participated in the relationship.

same entity

relationship; degree = 1)

ternary relationship

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.

Instructor Name Last Name First Name Middle Initial

Instructor ID Room ID Division Number Days of Week Start Time End Time

is assigned to

Data Modeling

System Concepts for Data Modeling

instances of another entity

primary key of one entity must be migrated into the other entity

as a foreign key.

(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

Data Modeling

System Concepts for Data Modeling

between parent and child it is called a non-specific relationship

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.

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)

Gender (Subsetting Criteria 1)

Race (Subsetting Criteria 2)

Grade Point Average

Student Number (Primary Key 1)

Name (Alternate Key 1)

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)

Current Candidate for Degree?

Data Modeling

System Concepts for Data Modeling

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.

that are common to one or more entity subtypes

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

System Concepts for Data Modeling

common attributes from an entity supertype, and then add other attributes that are unique to an instances of the subtype.

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

domains, and defaults of those attributes.

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)

all attributes from PERSON plus

Pension Plan Code Life Insurance Plan Code Medical Insurance Plan Code Vacation Days Accumulated Sick Days Acculumlated

ADDRESS

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?

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

has earned

Data Modeling

The Process of Logical Data

Modeling

on strategic information system plans

defines an overall vision and architecture for information systems

model

Data Modeling

The Process of Logical Data

Modeling

fundamental of entities

are not described in terms of keys or attributes

(depending on the planning methodology’s standards and the level

of detail desired by executive management)

non-specific.

repository

Data Modeling

The Process of Logical Data

Modeling

an application data model.

perspective

the study and definition phases of a project

details or technology, they may be constructed (through reverse

engineering) from existing databases.

systems analysis

FOCUS ON SYSTEM PROCESSES

FOCUS ON SYSTEM INTERFACES

Existing Applications and Technology

Existing Interfaces and Technology

Existing Networks and Technology

FOCUS ON SYSTEM GEOGRAPHY

Definition Phase (establish and prioritize business system requirements)

Study Phase (establish system improvement objectives)

Survey Phase (establish scope and project plan)

Customers order zero,

one, or more products

Products may be ordered

by zero, one, or more

customers.

Reverse Engineering (optional)

Data Modeling

The Process of Logical Data

Modeling

analysis Most analysts prefer to draw process models to document the current system

following reasons:

more completely than process models.

models.

Process models often require dozens of sheets of paper.

Data Modeling

The Process of Logical Data

Modeling

following reasons: (continued)

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

no attributes – a context data model.

into details about the entities and business rules

Data Modeling

The Process of Logical Data

Modeling

stages:

associative entities, include primary, alternate keys, and foreign keys, plus precise cardinalities and any generalization hierarchies

attributes and subsetting criteria.

domains, and defaults.

Data Modeling

The Process of Logical Data

Modeling

data requirements, not technical solutions

way to implement those requirements with database technology

into a physical data model (called a database schema) for the

chosen database management system

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

and flexibility through a process called normalization

information as supplied by the user community

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

simultaneously constructing and verifying the data models is

Joint Application Development (JAD)

Data Modeling

Discover the system

entities

What are the subjects of the business? In other words, what types of persons, organizations, organizational units, places, things, materials, or events are used in, or interact with this system, 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 an

instance of each subject from other instances of the same subject? Are there any plans to change this identification scheme in the future?

Discover entity subsetting

criteria Are there any characteristics of a subject that divide allinstances of the subject into useful subsets? Are there any

subsets of the above subjects for which you have no convenient way to group instances?

Discover attributes and

domains

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

Discover security and

control needs

Are there any restrictions on who can see or use the data? Who

is allowed to create the data? Who is allowed to update the data? Who is allowed to delete the data?

Discover data timing

needs

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

Discover generalization

hierarchies

Are all instances of each subject the same? That is, are there special types of each subject that are described or handled differently? Can any of the data be consolidated for sharing? Discover relationships

and degrees

What events occur that imply associations between subjects? What business activities or transactions require involve handling or changing data about several different subjects of the same or a different type?

Discover cardinalities Is each business activity or event handled the same way or are

there special circumstances? Can an event occur with only