Chapter10 conceptual data modeling

Copyright 2002 Prentice-Hall, Inc.
Modern Systems Analysis
and Design
Third Edition
Jeffrey A. Hoffer
Joey F. George
Joseph S. Valacich
Chapter 10
Structuring System
Requirements:
Conceptual Data Modeling
10.1

Learning Objectives
Define key data modeling terms
 Entity type
 Attribute
 Multivalued attribute
 Relationship
 Degree
 Cardinality
 Business Rule
 Associative entity
 Trigger
 Supertype
 Subtype10.2

Learning Objectives
Learn to draw Entity-Relationship (E-R)
Diagrams Review the role of conceptual data
modeling in overall design and analysis of an
information system
Distinguish between unary, binary, and
ternary relationships, and give an example of
each
Define four basic types of business rules in
an E-R diagram
10.3

Learning Objectives
Explain the role of CASE technology in
the analysis and documentation of data
required in an information system
Relate data modeling to process and
logic modeling as different views
describing an information system
10.4

Representation of organizational data
Purpose is to show rules about the meaning and
interrelationships among data
Entity-Relationship (E-R) diagrams are commonly
used to show how data are organized
Main goal of conceptual data modeling is to create
accurate E-R diagrams
Methods such as interviewing, questionnaires and
JAD are used to collect information
Consistency must be maintained between process
flow, decision logic and data modeling descriptions
10.5

Process of Conceptual Data
Modeling
First step is to develop a data model for the
system being replaced
Next, a new conceptual data model is built
that includes all the requirements of the new
system
In the design stage, the conceptual data
model is translated into a physical design
Project repository links all design and data
modeling steps performed during SDLC
10.6

Deliverables and Outcome
Primary deliverable is the entity-relationship
diagram
There may be as many as 4 E-R diagrams
produced and analyzed during conceptual
data modeling
 Covers just data needed in the project’s
application
 E-R diagram for system being replaced
 An E-R diagram for the whole database from
which the new application’s data are extracted
 An E-R diagram for the whole database from
which data for the application system being
replaced is drawn10.7

Figure 10-3
Sample conceptual data model diagram
10.8

Deliverables and Outcome
Second deliverable is a set of entries about
data objects to be stored in repository or
project dictionary
 Repository links data, process and logic models of
an information system
 Data elements that are included in the DFD must
appear in the data model and visa versa
 Each data store in a process model must relate to
business objects represented in the data model
10.9

Gathering Information for
Two perspectives
 Top-down
 Data model is derived from an intimate
understanding of the business
 Bottom-up
 Data model is derived by reviewing
specifications and business documents
10.10

Introduction to Entity-
Relationship (E-R) Modeling
Notation uses three main constructs
 Data entities
 Relationships
 Attributes
Entity-Relationship (E-R) Diagram
 A detailed, logical representation of the
entities, associations and data elements for
an organization or business
10.11

Entity-Relationship (E-R)
Modeling
Key Terms
Entity
 A person, place, object, event or concept in the
user environment about which the organization
wishes to maintain data
 Represented by a rectangle in E-R diagrams
Entity Type
 A collection of entities that share common
properties or characteristics
Attribute
 A named property or characteristic of an entity that
is of interest to an organization
10.12

Modeling
Key Terms
Candidate keys and identifiers
 Each entity type must have an attribute or
set of attributes that distinguishes one
instance from other instances of the same
type
 Candidate key
 Attribute (or combination of attributes) that
uniquely identifies each instance of an entity
type
10.13

Modeling
Key Terms
Identifier
 A candidate key that has been selected as the
unique identifying characteristic for an entity type
 Selection rules for an identifier
1. Choose a candidate key that will not change its value
2. Choose a candidate key that will never be null
3. Avoid using intelligent keys
4. Consider substituting single value surrogate keys for
large composite keys
10.14

Modeling
Key Terms
Multivalued Attribute
 An attribute that may take on more than
one value for each entity instance
 Represented on E-R Diagram in two ways:
 double-lined ellipse
 weak entity
10.15

Modeling
Key Terms
Relationship
 An association between the instances of
one or more entity types that is of interest
to the organization
 Association indicates that an event has
occurred or that there is a natural link
between entity types
 Relationships are always labeled with verb
phrases
10.16

and the E-R Diagram
Goal
 Capture as much of the meaning of the data as
possible
Result
 A better design that is easier to maintain
10.17

Degree of Relationship
Degree
 Number of entity types that participate in a
relationship
Three cases
 Unary
 A relationship between two instances of one entity type
 Binary
 A relationship between the instances of two entity types
 Ternary
 A simultaneous relationship among the instances of
three entity types
 Not the same as three binary relationships
10.18

Figure 10-6
Example relationships of different degrees
10.19

Cardinality
The number of instances of entity B that can
be associated with each instance of entity A
Minimum Cardinality
 The minimum number of instances of entity B that
may be associated with each instance of entity A
Maximum Cardinality
 The maximum number of instances of entity B that
may be associated with each instance of entity A
10.20

Naming and Defining
Relationships
Relationship name is a verb phrase
Avoid vague names
Guidelines for defining relationships
 Definition explains what action is being taken and
why it is important
 Give examples to clarify the action
 Optional participation should be explained
 Explain reasons for any explicit maximum
cardinality
10.21

Naming and Defining
Relationships
Guidelines for defining relationships
 Explain any restrictions on participation in
the relationship
 Explain extent of the history that is kept in
the relationship
 Explain whether an entity instance involved
in a relationship instance can transfer
participation to another relationship
instance
10.22

Associative Entity
An entity type that associates the
instances of one or more entity types
and contains attributes that are peculiar
to the relationship between those entity
instances
10.23

Domains
The set of all data types and ranges of
values that an attribute can assume
Several advantages
1. Verify that the values for an attribute are
valid
2. Ensure that various data manipulation
operations are logical
3. Help conserve effort in describing
attribute characteristics
10.24

Triggering Operations
An assertion or rule that governs the validity of data
manipulation operations such as insert, update and delete
Includes the following components:
 User rule
 Statement of the business rule to be enforced by the
trigger
 Event
 Data manipulation operation that initiates the operation
 Entity Name
 Name of entity being accessed or modified
 Condition
 Condition that causes the operation to be triggered
 Action
 Action taken when the operation is triggered
10.25

Responsibility for data integrity lies
within scope of database management
system, not individual applications
10.26

The Role of CASE in
Conceptual Data
CASE tools provide two important
functions:
 Maintain E-R diagrams as a visual
depiction of structured data requirements
 Link objects on E-R diagrams to
corresponding descriptions in a repository
10.27

Summary
Process of conceptual data modeling
 Deliverables
 Gathering information
Entity-Relationship Modeling
 Entities
 Attributes
 Candidate keys and identifiers
 Multivalued attributes
Degree of relationship
10.28

Summary
Cardinality
Naming and defining relationships
Associative entities
Domains
Role of CASE
10.29

Chapter10 conceptual data modeling

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Chapter10 conceptual data modeling