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DataBase Development and Implementation Lec02 - Conceptual Data Modeling, Study notes of Mobile Computing

"Detailed informtion about Conceptual Data Modeling, Systems Development techniques, Database Design Process, Phases of Database Design,Approaches for data model development, E-R Modeling."

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DBDI 30/05 /2007
Lecture 2 /CDM 1
DBDI / Lecture 2
Conceptual Data Modeling
Dr. Ala Al-Zobaidie
The slides are based on the textbooks
Database Systems by Connolly & Begg &
Fundamentals of Database Systems by Elmasri & Navathe
30/05/2007 Lecture 2 2
Lecture’s Objectives
System Development & Conceptual Data Model
Entity-Relationship Model concepts
Entity types
Attributes
Relationship types
Attributes on relationship
Structural Constraints (Cardinality & Participation)
Extended case studies
Problems with ER Models
The Enhanced E-R Model
Super-classes & Subclasses of Entity Type
Attribute Inheritance
Specialization & Generalization and their Constraints.
Further Case Studies with different ERD notations
30/05/2007 Lecture 2 3
Systems Development techniques
There are many different techniques available to
help you with the tasks of systems development.
Fall into three categories:
1. Functions
Modeling
2. Information
Modeling,
3. Cross
Referencing
which involves
modeling the
links between
Information
30/05/2007 Lecture 2 4
Conceptual, Logical & Physical Models
/1
Information & process modeling usually carried-
out separately and cross-checked.
Development result is kept in an operational
Database & applications can access it.
30/05/2007 Lecture 2 5
Conceptual, Logical & Physical Models
/2
Conceptual:
Develop formal models of the bu siness information & process
requirements.
Logical:
Transform the Conceptual Mode ls into definitions for the technical
environment
Physical Build phase:
you develop and
run Structured
Query Language
(SQL)
statements to
create the
physical
database objects
30/05/2007 Lecture 2 6
Data Modeling, ERD /1
Attempt to provide a representation of
(user) reality
Ignores some of the complexity of the real world
Simplicity attained by using small set of constructs
Attempt to reduce the organisation’s world
into a description of entities and
relationships
Simple description of information requirements that
can be used by the computer
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DBDI / Lecture 2

Conceptual Data Modeling

Dr. Ala Al-Zobaidie

The slides are based on the textbooks Database Systems by Connolly & Begg & Fundamentals of Database Systems by Elmasri & Navathe 30/05/2007 Lecture 2 2

Lecture’s Objectives

  • System Development & Conceptual Data Model
  • Entity-Relationship Model concepts
    • Entity types
    • Attributes
    • Relationship types
    • Attributes on relationship
    • Structural Constraints (Cardinality & Participation)
  • Extended case studies
  • Problems with ER Models
  • The Enhanced E-R Model
    • Super-classes & Subclasses of Entity Type
    • Attribute Inheritance
    • Specialization & Generalization and their Constraints.
  • Further Case Studies with different ERD notations

30/05/2007 Lecture 2 3

Systems Development techniques

  • There are many different techniques available to

help you with the tasks of systems development.

  • Fall into three categories:

1. Functions

Modeling

2. Information

Modeling,

3. Cross

Referencing

which involves

modeling the

links between

Information

30/05/2007 Lecture 2 4

Conceptual, Logical & Physical Models

/

  • Information & process modeling usually carried-

out separately and cross-checked.

  • Development result is kept in an operational

Database & applications can access it.

30/05/2007 Lecture 2 5

Conceptual, Logical & Physical Models

/

  • Conceptual:
    • Develop formal models of the business information & process
requirements.
  • Logical:
    • Transform the Conceptual Models into definitions for the technical
environment
 Physical Build phase:
  • you develop and
run Structured
Query Language
(SQL)
statements to
create the
physical
database objects

30/05/2007 Lecture 2 6

Data Modeling, ERD /

  • Attempt to provide a representation of

( user ) reality

  • Ignores some of the complexity of the real world
  • Simplicity attained by using small set of constructs
  • Attempt to reduce the organisation’s world

into a description of entities and

relationships

  • Simple description of information requirements that

can be used by the computer

30/05/2007 Lecture 2 7

Data modeling, ERD /

  • ER modeling process is independent of

the development platform (or software)

  • Enables unambiguous, accurate communication of

understanding of the data resource in an abstract

level

  • It is used for communications between

database designer & users during system

analysis & design process

30/05/2007 Lecture 2 8

Operations & Maintenance^ Operations & Maintenance
ImplementationImplementation
FeasibilityFeasibility
AnalysisAnalysis
DesignDesign
StrategyStrategy

Data modeling in the Systems

Development Lifecycle

Conceptual
high-level data model
used in support of the strategy,
& possibly refined in
the feasibility study
“ What ”

Implement the transformed Logical model into a Physical Model “ Do it

Transform CDM into Logical Data Model “ How

30/05/2007 Lecture 2 9

Database Design

Process

30/05/2007 Lecture 2 10

  • Requirements collection and analysis
    • Database Requirements
    • Functional Requirements (operations on database)
  • Conceptual Design & Functional Analysis
    • Create a conceptual schema ( High-Level ), e..g. ERD
    • High level transaction specification corresponding to operations on
database
  • Logical Design
    • Map conceptual database schema to logical database schema
( Representational ), it is also called an Implementation model e.g.
Relational
  • Physical Design
    • Internal storage structures and file organisations are specified
( Physical ), e.g. B-tree
  • Application Program Design &
  • Transaction Implementation

Phases of Database Design

in parallel
to physical design

30/05/2007 Lecture 2 11

Approaches for data model

development

  • Top-down Approach
    • 3 steps
      • identify data entities
      • determine attributes of the entities
      • determine the nature of the relationships
    • usually results in a data model that is well organized

but details can be easily overlooked.

  • Bottom-up Approach
    • gather information on data used by the organization by...
    • group into entities of which these data are attributes
    • determine the nature of the relationships
  • insures that no important data is overlooked but

overall organization may not be so apparent.

30/05/2007 Lecture 2 12

E-R Modeling

  • E/R model consists of:
    • Entity type
    • Attribute type
    • Relationship type

30/05/2007 Lecture 2 19

 In general, composite and multiple-valued attributes

may be nested arbitrarily to any number of levels

although this is rare.

Address

Street
Address
City District Post Code
Number Street
House
Number

Nested attribute

30/05/2007 Lecture 2 20

Relationship

  • Relationship = An association among

entities

  • Degree of a relationship = Number of

entities involved with the relationship

STUDENT LECTURER

Taught by

30/05/2007 Lecture 2 21

 A relationship where the same entity

participates more than once in a different

roles.

PERSON Married to EMPLOYEE Manages

1-TO-1 1-TO-MANY

Unary or Recursive Relationships

Staff

Supervises
Supervisee
Supervisor

30/05/2007 Lecture 2 22

Binary Relationships

EMPLOYEE
PARKING
SPOT

is given

PRODUCT
LINE

Contains PRODUCT

STUDENT COURSE

Enrol in

1-TO-
1-TO-MANY
MANY-TO-MANY

30/05/2007 Lecture 2 23

Ternary Relationships

VENDOR Ships WAREHOUSE

PART

30/05/2007 Lecture 2 24

Participation & Structural Constraints

  • Two types: cardinality and participation

constraints.

  • Cardinality Constraints (Ratio)
    • Determines the number of possible relationships for each
participating entity.
  • The number of allowed instances of entity B that can (or
must) be associated with each instance entity A.
  • Most common degree for relationships is binary with
cardinality ratios of one-to-one (1:1), one-to-many (1:M) or
many-to-many (M:N).
  • Participation Constraints
    • Determines whether the existence of an entity depends on its
being related to another entity through the relationship.

30/05/2007 Lecture 2 25

IsAllocated Staff

Branch

M

Branch_No
Staff_No

Participation Constraints of

Branch IsAllocated Staff Relationship

30/05/2007 Lecture 2 26

IsAllocated Staff

Branch

Branch_No
Staff_No

Displaying Participation Constraints using

(Min, Max) Notation

30/05/2007 Lecture 2 27

Another notation

CUSTOMER Rent VIDEO
EMPLOYEE PROJECT

Assigned to

PERSON

Married to

30/05/2007 Lecture 2 28

Entities associated through two distinct

Relationships

30/05/2007 Lecture 2 29

Relationship called Views with attributes

30/05/2007 Lecture 2 30

ER-diagram Chen Notation

30/05/2007 Lecture 2 37

Semantic Net of ER Model with Fan

Trap

30/05/2007 Lecture 2 38

Staff IsAllocated^

Branch

M

Operates

M

Staff

IsAllocated

1

Branch

M

Division

Operates

1

M Division

Restructuring ER model to remove Fan

Trap

30/05/2007 Lecture 2 39

Semantic Net of Restructured ER

Model with Fan Trap Removed

30/05/2007 Lecture 2 40

Branch

IsAllocated

Property_

for_Rent

M

Staff

Oversees

M

An Example of a Chasm Trap

30/05/2007 Lecture 2 41

Semantic Net of ER Model with Chasm

Trap

30/05/2007 Lecture 2 42

Branch

IsAllocated

Property_

for_Rent

M

Staff

Oversees

M

Has

1 M

ER Model restructured to remove

Chasm Trap

30/05/2007 Lecture 2 43

Semantic Net of Restructured ER Model

with Chasm Trap Removed

30/05/2007 Lecture 2 44

The Enhanced Entity-Relationship Model

  • Since 1980s , new demanding applications

emerged and hence new requirements.

  • Basic concepts of ERM are not sufficient to

represent the requirements of the newer, more

complex applications.

  • Response is development of additional

‘semantic’ modelling concepts.

  • Semantic concepts are incorporated into the

original ER model and is called the Enhanced

Entity-Relationship (EER) model.

30/05/2007 Lecture 2 45

The Enhanced Entity-Relationship Model

  • EERM also useful to represent many real-

world objects that can be classified

naturally into hierarchies.

  • Additional concepts of EER model

includes Specialization / Generalization, &

categorisation.

30/05/2007 Lecture 2 46

Concept of Specialization /

Generalization

  • Superclass
    • it is an entity type
    • it is at a high level (Generalisation)
    • It includes distinct subclasses that require to

be represented in a data model.

  • it is also known as a Supertype

30/05/2007 Lecture 2 47

Concept of Specialization /

Generalization

  • Subclass
    • it is an entity type
    • it is at a lower level (Specialisation)
    • it is a subset of a supertype
    • it has a distinct role
    • It is also known as a subtype
    • the relationship with a superclass represents

an “IS-A” relationship

  • it shares common attributes or relationships

distinct from other subsets

30/05/2007 Lecture 2 48

Concept of Specialization / Generalization

  • An entity in a subclass may possess subclass

specific attributes, as well as those associated

with the superclass.

  • Attribute Inheritance
  • Generalization process
    • The process of minimising the differences

between entities by identifying their common

features.

30/05/2007 Lecture 2 55

A Shared Subclass called Sales_Trainee

30/05/2007 Lecture 2 56

Categorization:

Property_Owner & Property Categories

30/05/2007 Lecture 2 57

Property represented as a Specialization / Generalization.

30/05/2007 Lecture 2 58

Aggregation Relationships

A wheel , a seat or a chassis is not type of a car;

rather each is part of a car, or

A car has a …

A keeper is a member of a Soccer team, …

Wheels Chassis Seats
Car

Aggregation: Is_Part_Of Association: Is_Member_Of

Keeper Defender Striker
Soccer
Team

30/05/2007 Lecture 2 59

Manager’s View of DreamHome Case Study

Building an EER Model

  • Identify entity types.
  • Identify relationship types.
  • Determine cardinality and participation

constraints of relationship types.

  • Identify and associate attributes with entity or

relationship types.

  • Determine candidate and primary key attributes.
  • Specialize / generalize entity types.
  • Identify Category, Aggregation & Association

entity types.

  • Draw the EER Diagram.