1

Data Modelling

Dr. Chaitali Basu Mukherji

2

Data Modeling Concepts

A data model is a simply a diagram that describes the most important “things” in your business environment from a data-centric point of view.

3

Data Model

• for representation of a part of a real world• it is an abstraction of the reality : ignores

unnecessary details• represents operational data about real world

events, entities, activities, etc. • model may be at various levels depending of

requirements : – logical or physical– external, conceptual, internal

4

Data Model……

• a good model– is easy to understand– has a few concepts– permits top-down specifications

• model offers concepts, constructs and operations

• must capture meaning of data (data semantics) which help us in interpreting and manipulating data

5

Data Model……

• semantics captured through data types, inter-relationships and data integrity constraints– uniqueness– existence dependence– restrictions on some operations such as insertions,

deletions

6

Example : Data Model in a PL

• data structuring concepts– data field/variable– data groups and arrays– Record/structure : unit of file i/o– file : collection of records

7

Example : Data Model in a PL …

• Operations– file level : open, close– record level :

• read next/random• write next/random

– field level : computations• no inter-file and inter-record relationships• no constraints except primary key for indexed

files

8

Why We Model We build models of complex systems because we

cannot comprehend any such system in its entirety Need to develop a common understanding of the

problem and the solution Cannot afford a trial-and-error approach to communicate the desired structure and behavior of

our systems to visualize and control system’s architecture to understand the system we are building, often

exposing opportunities for simplification and reuse

9

Why We Modelto manage risk The choice of which model we use has a profound

influence on how a problem is attacked and how a solution is shaped

No single model is sufficient; every complex system is best approached through a set of independent models

Every model may be expressed at different levels of fidelity

The best models are connected to reality

10

Conceptual Data ModelingData can be modeled at many levels, including the

conceptual, logical and physical level.

Conceptual Data Modeling is a very high level representation of organizational data. The purpose is to show the basic building blocks for the organization, i.e.

the entities and rules about their meaning and interrelationships

Logical data modeling adds more detail to conceptual modeling, but is still concerned only with how the organization/business uses data.

Physical data modeling adds more detail, but is especially concerned with the actual physical implementation of the data.

11

Gathering InformationTwo perspectives

Top-downData model is derived from an intimate understanding of the

businessBottom-up

Data model is derived by reviewing specifications and business documents

12

Four Types of Data Models

Current System Proposed SystemProject-Level

E-R diagram for system being replaced

Covers just data needed in the project’s application

Enterprise-Level

An E-R diagram for the whole database from which data for the application system being replaced is drawn

An E-R diagram for the whole database from which the new application’s data are extracted

13

Sample conceptual data model diagram

14

Business Principles

Business PrinciplesOrganizational consensusData integrityImplementation efficiencyUser friendlinessOperational efficiency

IT PrinciplesScalabilityCompliance with IT standards

15

Primary steps

Problem definitionDo I need data warehouse?What specific problems will it solve?What are my available resources (time, money,

and personnel)?What criteria will I use to measure success?Should I outsource all, some, or none of the

development and operation?Am I upgrading an existing system, converting

from a legacy system, or developing from scratch?

16

Primary steps

Requirement analysisGroup and "bubble-up" requirements.Generate a prioritized requirements table listing the

requirement, where it came from, the success criteria, and priority. Keep this table high-level. A table with a dozen requirements will be much easier to manage than one with hundreds.

Produce a detailed development schedule including hardware, software, personnel, documentation, and reviews. Include outsourcing requirements and long lead-time items.

Get a sign-off of the requirements, resource allocation, and schedule from top management before you go any further.

17

Primary stepsRequirement analysis

Clearly state the problem(s) you wish to solve.Identify all data sources and formats.Identify the users of the completed system.Formulate a specific budget - time, money, personnel.Ask identified users to specifically state what they expect

the system to do.Ask management to specifically state their success

criteriaSeparate their requirements from their "desirements."

Only design to requirements. The enhancement phase is where you address the "desirements."

18

Primary stepsConceptual design

Information or data modelingFirst level – ER ModelingSecond level – Normalization

Design and prototypingRapid prototypingStructured development

19

Primary stepsDevelopment and DocumentationTest and ReviewDeployment and TrainingOperationEnhancementHelp Desk

20

Objectives of ER Model

• Define terms related to entity relationship modeling, including entity, entity instance, attribute, relationship and cardinality, and primary key.

• Describe the entity modeling process.• Discuss how to draw an entity relationship

diagram.• Describe how to recognize entities, attributes,

relationships, and cardinalities.

21

Database Model

A database can be modeled as:– a collection of entities,– relationship among entities.

Database systems are often modeled using an Entity Relationship (ER) diagram as the "blueprint" from which the actual data is stored — the output of the design phase.

22

Entity Relationship Diagram (ERD)• ER model allows us to sketch database designs

• ERD is a graphical tool for modeling data.• ERD is widely used in database design • ERD is a graphical representation of the logical

structure of a database • ERD is a model that identifies the concepts or entities

that exist in a system and the relationships between those entities

23

Purposes of ERDAn ERD serves several purposes• The database analyst/designer gains a better

understanding of the information to be contained in the database through the process of constructing the ERD.

• The ERD serves as a documentation tool.• Finally, the ERD is used to communicate the

logical structure of the database to users. In particular, the ERD effectively communicates the logic of the database to users.

24

Components of an ERD

An ERD typically consists of four different graphical components:1. Entity2. Relationship3. Cardinality4. Attribute

25

Classification of Relationship

• Optional Relationship– An Employee may or may not be assigned to a

Department– A Patient may or may not be assigned to a Bed

• Mandatory Relationship– Every Course must be taught by at least one

Teacher– Every mother have at least a Child

26

Cardinality Constraints Express the number of entities to which another

entity can be associated via a relationship set.• Cardinality Constraints - the number of instances of

one entity that can or must be associated with each instance of another entity.

• Minimum Cardinality– If zero, then optional– If one or more, then mandatory

• Maximum Cardinality– The maximum number

27

Cardinality Constraints (Contd.)

• For a binary relationship set the mapping cardinality must be one of the following types:–One to one

• A Manager Head one Department and vice versa

–One to many ( or many to one)• An Employee Works in one Department or One

Department has many Employees

–Many to many • A Teacher Teaches many Students and A student is

taught by many Teachers

28

Cardinality Constraints (Contd.)

29

Cardinality Constraints Example

• In our model, we wish to indicate that each school may enroll many students, or may not enroll any students at all.

• We also wish to indicate that each student attends exactly one school. The following diagram indicates this optionality and cardinality:

30

Cardinality Constraints Example (Contd.)

SCHOOL

STUDENTEach school enrolls

at least zero

and at most many

students

Each student attends

at least one

and at most one

school

31

Developing an ERDThe process has ten steps:

1. Identify Entities2. Find Relationships3. Draw Rough ERD4. Fill in Cardinality5. Define Primary Keys6. Draw Key-Based ERD7. Identify Attributes8. Map Attributes9. Draw fully attributed ERD10. Check Results

32

A Simple Example

A company has several departments. Each department has a supervisor and at least one employee. Employees must be assigned to at least one, but possibly more departments. At least one employee is assigned to a project, but an employee may be on vacation and not assigned to any projects. The important data fields are the names of the departments, projects, supervisors and employees, as well as the supervisor and employee number and a unique project number.

33

Identify entities• One approach to this is to work through the information

and highlight those words which you think correspond to entities.

• A company has several departments. Each department has a supervisor and at least one employee. Employees must be assigned to at least one, but possibly more departments. At least one employee is assigned to a project, but an employee may be on vacation and not assigned to any projects. The important data fields are the names of the departments, projects, supervisors and employees, as well as the supervisor and employee number and a unique project number.

• A true entity should have more than one instance

34

Find Relationships

• Aim is to identify the associations, the connections between pairs of entities.

• A simple approach to do this is using a relationship matrix (table) that has rows and columns for each of the identified entities.

35

Find Relationships (Contd.)

• Go through each cell and decide whether or not there is an association. For example, the first cell on the second row is used to indicate if there is a relationship between the entity "Employee" and the entity "Department".

36

Identified Relationships Names placed in the cells are meant to

capture/describe the relationships. So you can use them like this

• A Department is assigned an employee• A Department is run by a supervisor• An employee belongs to a department• An employee works on a project• A supervisor runs a department• A project uses an employee

37

Draw Rough ERDDraw a diagram and:• Place all the entities in rectangles• Use diamonds and lines to represent the

relationships between entities.• General Examples

38

Drawing Rough ERD (Contd.)

39

Drawing Rough ERD (Contd.)

40

Drawing Rough ERD (Contd.)

41

Fill in Cardinality• Supervisor

– Each department has one supervisor.• Department

– Each supervisor has one department.– Each employee can belong to one or more departments

• Employee– Each department must have one or more employees– Each project must have one or more employees

• Project– Each employee can have 0 or more projects.

42

Fill in Cardinality (Contd.)

The cardinality of a relationship can only have the following values

–One and only one–One or more–Zero or more–Zero or one

43

Cardinality Notation

44

Cardinality Examples

A

A

A

A

B

B

B

B

Each instance of A is related to a minimum ofzero and a maximum of one instance of B

Each instance of B is related to a minimum ofone and a maximum of one instance of A

Each instance of A is related to a minimum ofone and a maximum of many instances of B

Each instance of B is related to a minimum ofzero and a maximum of many instances of A

45

ERD with cardinality

46

Examples

47

ERD for Course Enrollment

48

ERD for Course Registration

49

Rough ERD Plus Primary Keys

50

Identify Attributes

• In this step we try to identify and name all the attributes

essential to the system we are studying without trying to

match them to particular entities.

• The best way to do this is to study the forms, files and

reports currently kept by the users of the system and

circle each data item on the paper copy.

51

Identify Attributes• Cross out those which will not be transferred to the new system,

extraneous items such as signatures, and constant information

which is the same for all instances of the form (e.g. your company

name and address). The remaining circled items should represent

the attributes you need. You should always verify these with your

system users. (Sometimes forms or reports are out of date.)

• The only attributes indicated are the names of the departments,

projects, supervisors and employees, as well as the supervisor and

employee NUMBER and a unique project number.

52

Map Attributes• For each attribute we need to match it with exactly one

entity. Often it seems like an attribute should go with more than one entity (e.g. Name). In this case you need to add a modifier to the attribute name to make it unique (e.g. Customer Name, Employee Name, etc.) or determine which entity an attribute "best' describes.

• If you have attributes left over without corresponding entities, you may have missed an entity and its corresponding relationships. Identify these missed entities and add them to the relationship matrix now.

53

Map Attributes (Contd.)

54

Draw Fully Attributed ERD

55

Check ERD Results

• Look at your diagram from the point of view of a system owner or user. Is everything clear?

• Check through the Cardinality pairs.• Also, look over the list of attributes associated

with each entity to see if anything has been omitted.