ITS232
Introduction To Database Management Systems
CHAPTER 2
Data Models
Siti Nurbaya Ismail
Faculty of Computer Science & Mathematics,
Universiti Teknologi MARA (UiTM), Kedah
| sitinurbaya@kedah.uitm.edu.my | http://www.sitinur151.wordpress.com |
| A2-3039 | ext:2561 | 012-7760562 |
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Chapter 2: Data Models
2.0 DATA MODELS
2.1 The Importance of Data Models
2.2 Data Model Basic Building Blocks
2.3 Business Rules
2.4 The Evolution of Data Models
2.5 Degrees of Data Abstraction
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2.0 Data Models
2.1 The Importance of Data Models
Data models
 Relatively simple representations, usually graphical of complex real-world
data structures
 Facilitate interaction among the designer, the applications programmer, and
the end user
 End-users have different views and needs for data
 Data model organizes data for various users
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2.0 Data Models
2.2 Data Model Basic Building Blocks
Entity
anything
about which
data are to be
collected and
stored
Attribute
a
characteristic
of an entity
Relationship
describes an
association
among
entities
Constraint
a restriction
placed on the
data
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2.0 Data Models
2.2 Data Model Basic Building Blocks
Entity
Attribute
Relationship
Constraint
Student
Subject
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2.0 Data Models
2.3 Business Rules
 Brief, precise, and unambiguous descriptions of policies, procedures, or
principles within a specific organization
 Apply to any organization that stores and uses data to generate
information
 Description of operations that help to create and enforce actions within
that organization’s environment
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2.0 Data Models
2.3 Business Rules





Must be rendered in writing
Must be kept up to date
Sometimes are external to the organization
Must be easy to understand and widely disseminated
Describe characteristics of the data as viewed by the company:
Entities
• corresponds to a table (ERD)
Relationships
• associations between entities
Attributes
• characteristics of entities
Connectivity
• describe the relationship classification
Constraints
• limitations on the type of data accepted
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2.0 Data Models
2.3 Business Rules: Discovering Business Rules
 Sources of Business Rules
– Company managers
– Policy makers
– Department managers
– Written documentation
• Procedures
• Standards
• Operations manuals
– Direct interviews with end users
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2.0 Data Models
2.3 Business Rules: Business Rules Example
 One student can register many subjects
 A painter can paint many paintings; each paining is painted by one painter.
A gallery can have many paintings. A painting can be exhibited by a gallery.
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2.0 Data Models
2.3 Business Rules:
Translating Business Rules into Data Model Components





Standardize company’s view of data
Constitute a communications tool between users and designers
Allow designer to understand the nature, role, and scope of data
Allow designer to understand business processes
Allow designer to develop appropriate relationship participation rules and
constraints
 Promote creation of an accurate data model
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2.0 Data Models
2.3 Business Rules: Discovering Business Rules




Generally, nouns translate into entities
Verbs translate into relationships among entities
Relationships are bi-directional
Fact finding techniques:
– The formal process of using techniques such as interview and questionnaire to
collect facts about system, requirements and preferences.
– To captures the essential facts necessary to build the required database
• What facts are collected?
– Captured facts about the current and/or future system
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2.0 Data Models
2.3 Business Rules: Fact Finding Techniques
Examining documents
Questionnaire
5 commonly
used fact finding
techniques
Research
Interviewing
Observation the
organization in
operations
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2.0 Data Models
2.4 The Evolution of Data Models
Hierarchical
Model
HierarchicalDatabase
Database
Model
••Represented
a group
of records
that to
relates
to each
Represented byby
a group
of records
that relates
each others
by a
pointer by a pointer  Root & Child
others
Network Database Model
Network Database Model
• Based on set theory, a set consists a collection of
• Based on set theory, a set consists a collection of records
records  Owner & Member
Relational
Model
RelationalDatabase
Database
Model
Based on
mathematical
concept
of relational
•• Based
onthe
the
mathematical
concept
of relational
Object-Oriented
Object-OrientedModel
Model
•• Based
onobject
object
oriented
concepts
Based on
oriented
concepts
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2.0 Data Models
2.4 The Evolution of Data Models
Hierachical Database Model


Developed in the 1960s to manage large amounts of data for complex
manufacturing projects
Basic logical structure is represented by an upside-down “tree” or by a
group of records that relates to each others by a pointer
– The uppermost record is a Root/Parent
– The lower record in a hierarchy is a Child
 Depicts a set of one-to-many (1:M) relationships between a parent and its
children segments
– Each parent can have many children
– each child has only one parent
 Can only support 1:1 and 1:M relationships where a child can only have one
parent
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2.0 Data Models
2.4 The Evolution of Data Models
Hierachical Database Model
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2.0 Data Models
2.4 The Evolution of Data Models
Advantages
of Hierachical Database Model
 Many of the hierarchical data
model’s features formed the
foundation for current data
models
 Its database application
advantages are replicated, albeit
in a different form, in current
database environments
 Generated a large installed
(mainframe) base, created a pool
of programmers who developed
numerous tried-and-true
business applications
Disadvantages
of Hierachical Database Model





Complex to implement
Difficult to manage
Lacks structural independence
Implementation limitations
Lack of standards
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2.0 Data Models
2.4 The Evolution of Data Models
Network Database Model
 Develop in 1970 in Conference on Data Systems Languages (CODASYL), by
Database Task Group (DBTG)
 Created to:
– Represent complex data relationships more effectively
– Improve database performance
– Impose a database standard
 Based on set theory, where a set consists of a collection of records
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2.0 Data Models
2.4 The Evolution of Data Models
Network Database Model
 Resembles hierarchical model
 Collection of records in 1:M relationships
 Composed of at least two record types
 Owner
– Equivalent to the hierarchical model’s parent
 Member
– Equivalent to the hierarchical model’s child
 Can support 1:1, 1:M and M:N relationships where a child can have more
than one parent.
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2.0 Data Models
2.4 The Evolution of Data Models
Network Database Model
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2.0 Data Models
2.4 The Evolution of Data Models
Network Database Model
 Disadvantages
– Too cumbersome
– The lack of ad hoc query capability put heavy pressure on programmers
– Any structural change in the database could produce havoc in all
application programs that drew data from the database
– Many database old-timers can recall the interminable information
delays
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
 Developed by Codd (IBM) in 1970, considered ingenious but impractical in
1970
 Conceptually simple, based on mathematical concept of relational
 Backwards, computers lacked power to implement the relational model
 Today, microcomputers can run sophisticated relational database software
 Relational Database Management System (RDBMS)
 Performs same basic functions provided by hierarchical and network DBMS
systems, in addition to a host of other functions
 Most important advantage of the RDBMS is its ability to hide the
complexities of the relational model from the user
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
Table
(relations)
• Matrix consisting of a series of row/column intersections
• Related to each other through sharing a common entity
characteristic
Relational
diagram
• Representation of relational database’s entities, attributes within
those entities, and relationships between those entities
Relational Table
• Stores a collection of related entities
• Resembles a file
Relational table
is purely logical
structure
• How data are physically stored in the database is of no concern to
the user or the designer
• This property became the source of a real database revolution
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
Example of table structure/relational table
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
Example of table with data/relational table
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
Example of table relationship/relational diagram
Relationship: An employee has one department.
One department has many employee.
EMPLOYEE
1
has
M
DEPARTMENT
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
 Rise to dominance due in part to its powerful and flexible query language
 Structured Query Language (SQL) allows the user to specify what must be
done without specifying how it must be done
 SQL-based relational database application involves:
– User interface
– A set of tables stored in the database
– SQL engine
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
Entity Relationship (E-R) Model
– Introduced by Chen in 1976
– Widely accepted and adapted graphical tool for data modeling
– Graphical representation of entities and their relationships in dB
structure
– Entity Relationship Diagram (ERD)
• Uses graphic representations to model database components
• Entity is mapped to a relational table
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2.0 Data Models
2.4 The Evolution of Data Models
Relational Model
Example of ERD
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2.0 Data Models
2.4 The Evolution of Data Models
Object Oriented Model
 Modeled both data and their relationships in a single structure known as an
object
 OO data model (OODM) is the basis for the OO database management
system (OODBMS)
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2.0 Data Models
2.4 The Evolution of Data Models
Object Oriented Model
 Object described by its factual content
– Like relational model’s entity
 Includes information about relationships between facts within object, and
relationships with other objects
– Unlike relational model’s entity
 Subsequent OODM development allowed an object to also contain all
operations
 Object becomes basic building block for autonomous structures
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2.0 Data Models
2.4 The Evolution of Data Models
Object Oriented Model





Object is an abstraction of a real-world entity
Attributes describe the properties of an object
Objects that share similar characteristics are grouped in classes
Classes are organized in a class hierarchy
Inheritance is the ability of an object within the class hierarchy to inherit
the attributes and methods of classes above it
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2.0 Data Models
2.4 The Evolution of Data Models
Object Oriented Model
A comparison of the OO model and the ER model
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2.0 Data Models
A Summary
 Each new data model capitalized on the shortcomings of previous models
 Common characteristics:
– Conceptual simplicity without compromising the semantic
completeness of the database
– Represent the real world as closely as possible
– Representation of real-world transformations (behavior) must comply
with consistency and integrity characteristics of any data model
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2.0 Data Models
A Summary
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2.0 Data Models
2.5 Degrees of Data Abstraction
 Way of classifying data models
– Data abstraction:
• hides extraneous information regarding data storage away from
database user
 Many processes begin at high level of abstraction and proceed to an everincreasing level of detail
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2.0 Data Models
2.5 Degrees of Data Abstraction

American National Standards Institute (ANSI) Standards Planning and
Requirements Committee (SPARC)

Three Level ANSI-SPARC Architecture
– Defined a framework for data modeling based on degrees of data
abstraction(1970s):
External
Conceptual
Internal
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2.0 Data Models
2.5 Degrees of Data Abstraction: Data Abstraction Levels
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2.0 Data Models
2.5 Degrees of Data Abstraction
Three Level ANSI-SPARC Architecture
-user’s view
External
Model
1.
External
level
User 1
User 2
View 1
View 2
User n
…
Model
2.Conceptual
Conceptual
level
-designer’s view
-h/w independent
-s/w independent
Conceptual Schema
3.Internal
Internal
level
Model
-DBMS’s view
-h/w independent
-s/w dependent
Internal Schema
Physical
data
Physical
Model
organization
-h/w dependent
-s/w dependent
View n
ERD
Database
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2.0 Data Models
2.5 Degrees of Data Abstraction: External Model
 End users’ view of the data environment
 Requires that the modeler subdivide set of requirements and constraints
into functional modules that can be examined within the framework of
their external models
Advantages:
– Easy to identify specific data required to support each business unit’s
operations
– Facilitates designer’s job by providing feedback about the model’s
adequacy
– Creation of external models helps to ensure security constraints in the
database design
– Simplifies application program development
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2.0 Data Models
2.5 Degrees of Data Abstraction: Conceptual Model
 Global view of the entire database  concept of the dB
– Describe what data is stored in the dB and relations among the data
 Data as viewed by the entire organization  logical structure 
communication tool between users and designer
 Basis for identification and high-level description of main data objects,
avoiding details  use as basic database bluprint
 Most widely used conceptual model is the entity relationship (ER) model
 Provides a relatively easily understood macro level view of data
environment
 Software and Hardware Independent
– Does not depend on the DBMS software used to implement the model
– Does not depend on the hardware used in the implementation of the
model
– Changes in either hardware or DBMS software have no effect on the
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database design at the conceptual level
2.0 Data Models
2.5 Degrees of Data Abstraction: Internal Model
 Representation of the database as “seen” by the DBMS
– Describes how the data is stored in the dB
 Maps the conceptual model to the DBMS
 Internal schema depicts a specific representation of an internal model
 Physical representation of the dB on the computer
 Software Dependent and Hardware Independent
– Depend on the DBMS software used to implement the model
– Does not depend on the hardware used in the implementation of the
model
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2.0 Data Models
2.5 Degrees of Data Abstraction: Internal Model
The Physical Model
 Operates at lowest level of abstraction, describing the way data are saved
on storage media such as disks or tapes
– how the data is stored in the database
 Software and Hardware Dependent
– Requires that database designers have a detailed knowledge of the
hardware and software used to implement database design
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2.0 Data Models
Summary
 A data model is a (relatively) simple abstraction of a complex real-world
data environment
 Basic data modeling components are:
– Entities
– Attributes
– Relationships
– Constraints
 Data modeling requirements are a function of different data views (global
vs. local) and level of data abstraction
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2.0 Data Models
Summary
Hierarchical
Model
HierarchicalDatabase
Database
Model
••Represented
a group
of records
that to
relates
to each
Represented byby
a group
of records
that relates
each others
by a
pointer by a pointer  Parent & Child  1:1 & 1:M
others
Network Database Model
Network Database Model
• Based on set theory, a set consists a collection of
• Based on set theory, a set consists a collection of records
records  Owner & Member  1:1& 1:M & M:N
Relational
Model
RelationalDatabase
Database
Model
Based on
mathematical
concept
of relational
•• Based
onthe
the
mathematical
concept
of relational
Object-Oriented
Object-OrientedModel
Model
•• Based
onobject
object
oriented
concepts
Based on
oriented
concepts
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Exercise 1
It is important to understand business rules when designing a database.
 Define business rules.
(1 marks)
 State TWO (2) reasons the importance of business rules in database
design.
(2 marks)
 Give ONE (1) example of business rules.
(1 marks)
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Exercise 2
Briefly explain the hierarchical and network database model, and what is the
difference between those two database models?
(6 marks)
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Exercise 3
What is the different between external, conceptual and internal model?
(7 marks)
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Exercise 4
What is a conceptual model and why it is important in database design?
(4 marks)
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