data modeling mit2

8/2/2019 Data Modeling MIT2

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Data modelingProf. Amos DAVIDhttp://ui-n2.loria.fr


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Course content Why data modeling ?

Entity-Relation model

Relational model

We will focus on

WHATWHY

HOW


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Why data modeling ? Illustration with an example


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From information problem

statement to data specification A case study

Problem statement We want an information system on students

Questions

What is an information system ?

What do we mean by students ?

Why do we want the information system ?

The system should provide answers to what questions ?


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What is an information system ? Functional characteristics of an information system

Store information (creation)

Retrieve information (access)

Update information (modification, deletion)

Components of an information system Users

End-user

Information system manager/administrator

Information base (database)

User interface To implement the functional characteristics

Between the end-user and the information base


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General schema of an IRS :

functional approach

Informationbase

User

Common access methods* navigation* query

Results

Informationproblem

Matching

Information problem transformation into access expression

Objects

Store /Update


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What do we mean bystudents ? Students viewed by who ?

Admission office ?

Post graduate school ?

Registrars office ? A university department ?

Alumni association ?

By a state government ?

Foreign government ?

By other structures ?

Students considered over what period ?

From admission to graduation only ?

Consider ex-students ?

University structures

Government structures


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Who are the end-users ?

This will determine how students are viewed

This will determine the final use of the informationto be accessed

Examples of end-users in the university structure

The VC

The registrar The Dean

The HOD

Any category of student (in-course, ex-student)


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Why data modeling ?

Represent the real world

Focus on the use of the elements

Represent only the necessary elements Represent the relationships between the selected

elements

DO NOT ignore or neglect necessary elements or

relations


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Why data modeling ?

For efficient computerization

Reduce data redundancy

Disk space problem

Volume of data transfer

Objects of documentation

Describe the computed elementsuser noticetechnical notice

For the programmer

For the system designer / manager For the end-users

Guaranty data integrity

Valid information irrespective of context


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Example on data integrity

Admission office

STUDENT (N, names, marital status, gender, age,degree, address)

The department

STUDENT (N, names, date of birth, courses, address)

The administrative office PERSONNEL (N, names, marital status, faculty,

department, address)


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General schema of an IRS :

Users and usage centered approach

Real world

Operation

Event

Data

Database Management System

(DBMS)

IRS

(What is seen by the end-user)

Data modeling

Developed using

Implemented for

Determined by


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Entity-Relation model


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Represent the real world elements with four

main concepts

Entity

Attributes

Relation Cardinality

Employs graphic representation Intuitive approach


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Entity

The basic conceptual or real element

Examples

A student

A personnel

A town

Entities have real existence (the instances)

They are identifiable Amos DAVID

Charles ROBERT

Ibadan


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Entity

Each entity is associated with a set of attributes

The instances of an entity have the same characteristics

They have the same set of attributes

Examples All students have the same set of attributes

All members of staff have the same set of attributes


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Attributes

Attributes are used for describing the entities

The entities and their attributes are determined according

to the database project

Taking into account the functions to be accomplished

Examples

Represent students at the department for course registration

Represents members of staff for salaries and promotions

One of the attributes must be an IDENTIFIER

Its value is unique for each entity


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Attributes

How to reduce redundancy Avoid structured attributes

Structured attributes should be decomposed

Example NamesFirst name, last name AddressStreet n, street name, town, local government, state

Decomposing structured attributes allows an easy access to thecomponent elements

Example The town element of an address can be easily extracted instead of performing

string extraction on the structured element


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Attributes

Examples Name, Address

Amos DAVID;Dept computer science, UI Ibadan, Ibadan, Oyo state

Olu OJO;23 Aderemo street, Agbowo, AgbowoLGA, Ibadan, Oyo state Uche KALU;5 market road, Anambra, Anambra state

Problems with this representation

The addresses do not have the same number of elements, so

how can one obtain a specific component ?

The nth element ?

Starting from the nth character ?

How can one locate the town within an address ?


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Attributes Examples

Dissociate structured elements Name, Street number, Street name, Town, Local government, State

Amos DAVID; Dept computer science; UI Ibadan; Ibadan; ;Oyo state

Olu OJO; 23; Aderemo street; Agbowo; Ibadan; ; Oyo state

Uche KALU; 5; market road; Anambra; ; Anambra state

Efficiency Each entry has the same number of elements

A component element can be easily extracted using its position

Example The town value is always at the 4th position

The state value is always at the last position

The position can be in string functions or for the colon numbers in tables


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Attributes

reducing redundancy

Avoid attributes whose value is a list ;a new entity should be created

Example (memory redundancy)

Courses as attribute ofDegree

We do not know the number of courses for a degree

Create DEGREE and COURSE

Associate the two entities (to be seen later)


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Example (memory redundancy)

Computer science, course 1, course 2, course 3

Biology, course 3, course 6, course 7, course 20

Chemistry, course 7, course 3, course 8, course 9, course 10

In terms of memory allocation, how many courses should be

anticipated ?

Because of the unknown number of courses, the anticipated number

will either be too few or too many


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Attributes

How to ensure data integrity

Identify the functional dependency between attributes

Example of dependency

A town belongs to only one state

Towns are unique

there is dependency between town and state if the town is known, the state can be determined

unambiguously

In a case of functional dependency, create a new entity to regroup

the dependent attributes

Create a relation between the new entity and the original one


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Attributes

Examples Name, Street number, Street name, Town, Local government, State

1.Amos DAVID; Dept computer science; UI Ibadan; Ibadan; Oyo state

2.Olu OJO; 23; Aderemo street; Agbowo; Ibadan; Oyo state

3.Samuel UCHE; 213 Sango road; Dugbe; Ibadan; Oyo state 4.Uche KALU; 5; market road; Anambra; ; Anambra state

Entities 1, 2 and 3 are redundant, prone to non integrity Entering entities 1, 2 and 3 (town, state) three times may produce

typographical error

Should a town change from one state to another, all the entities are no longervalid All the entities must be modified


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Entitygraphical representation

An entity is represented by a rectangle divided into two parts

The name of the entity is represented at the upper part

The names of the attributes are represented at the lower part

The identifier is underlined

PERSON

NumberLast name

First name

Date of birth

TOWN

Town nameState

Local government


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Relation

A relation specifies the association between two

or more entities

Example

Town and Person

The relation should specify the semantic of the

association

Apersonlives ina town


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Relation

A relation is symbolized by an oval with its semantic inside the oval

A relation is further specified by cardinalities that indicate thenumber of associated instances

Example A person lives in a minimum of one town and in a maximum of 1 town

A town is inhabited by a minimum of one person and a maximum of n(indicating several)

PERSONNumber

Last name

First name

Date of birth

TOWNName

Surface area

State

Local government

Lives in(1,1)

(1,n)


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Relation

A relation may sometimes have an attribute

The attribute describes the relation and not theentities associated

Example

The numberof an article bought by a client as well as thedate are neither an attribute of the client nor that of thearticle, but an attribute of the association

The attributes of the relation are indicated at thelower part of the oval that represents the relation


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Relation

CLIENT

Number

Last nameFirst name

Date of birth

ARTICLE

Name

Unit priceBoughtQuantity

Date(1,n) (1,m)


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Relation

Maximum cardinality

This indicates the maximum cardinalities oneither side of a relation

Example

PERSONNumber

Last name

First name

Date of birth

TOWNName

Surface area

State

Local government

Lives in(1,1)

(1,n)

[n:1]


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Relation

How to read the relations : recall

A person lives in a minimum of 1 town and in a maximum

of 1 town

A town is inhabited (is lived) by a minimum of one

person and a maximum of n person (several)

PERSONNumber

Last name

First name

Date of birth

TOWNName

Surface area

State

Local government

Lives in(1,1)

(1,n)

[n:1]


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Proposed methodology

Identify an entity

Develop fully the entity and chose the

identifier Associate the entity with existing ones if and

where necessary

Establish the cardinalities

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Exercise

In a shopping center, a client can buy one or

more articles of various quantities. Propose an

ER model for representing the elements ofinformation necessary for billing the client.

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Relational model


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Relational model

The basic concepts

Relation

Domain Attribute

Key

N-uplet


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Relation (Table)

Last name First name Date of birth Degree

Attributes

Domain (same types of value :names)

N-Uplets

STUDENT


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Domain

Represents the data type of a column

Can be defined in form of intention or extension

In form of intention, it is specified by a formal

definition Example

Integer values

Character set of less than 20 characters

In form of extension, it is specified as a finite listof values

Example Town : {Oyo, Ibadan, Lagos}


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Relation

A relation R is represented as R(A1, , An)

Where

A1 takes its values from D1 An takes its values from Dm

m


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Attribute An attribute specifies a constituent of the relation

(a particular column of the table)

Attributes are unique within a relation(each column must be distinguished from theothers) Two columns should not have the same name

Two attributes may have the same domain Example

First name, Last name : NAMES


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Cartesian product of relation

Let R(A1, A2) be a relation

The Cartesian product of the relation represents allthe possible combinations of the values of the

attributes

Example

Cars-parked (Make, Color)

Where

Make makes of cars (Toyota, Peugeot)

Color colors of cars (red, black, white)


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Cartesian product of a relation

Toyota, red

Toyota, black

Toyota, white Peugeot, red

Peugeot, black

Peugeot, white


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The intention of a relation

The intention of a relation specifies how the

relation should be interpreted

Example

Cars-parked (Make, Color)

Cars parked in front of the department ofcomputer science, University of Ibadan


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N-uplet

Represents the extension of a relation

It is a Cartesian product of attributes

A line of the table

Also called a record

Example

Person (First name, Surname)

Amos, David

John, Olaoye


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Schema of a relation

The schema of a relation specifies the

intention of the relation and the associated

integrity constraints


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Constraint of data integrity Constraint on a single attribute

Example The values of vehicle makes should be German vehicles

Constraint based on two attributes Example

The date of marriage should be date of birth

Constraint on the table n-uplets Example

The number of registrations for a degree in one year should belimited to one


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Maximum Key

A set of attributes of a relation whose values aredistinct for each n-uplet

Example Person (Matriculation number, First name, Last

name, Date of birth, email)

All the attributes combined can form the maximumkey


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Minimum key (the key)

A key is the minimum set of attributes of a

relation whose values are distinct for each n-uplet

Examples

Student N, first name, last name

The Student N, first name combined can be used as key, but

only the Student N is sufficient

Student N, first name, last name, email

Either Student N or emailcan be used as key


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Relations in 1st normal form

A relation is said to be in 1st normal form if all

the attributes are of single values


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Relations in 2nd normal form

A relation is in 2nd normal form if and only if

it is in 1st normal form and there is no FD

between a subset of the key and the rest of theattributes

This mean that the key of the relation must be a

minimum key


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Relations in 3rd normal form

A relation is in 3rd normal form if it is in 2nd

normal form and there is no FD between non

key attributes


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From ERM to RM1. To each entity corresponds a relation

(Entity namerelation name)2. To each attribute of an entity corresponds an attribute of the relation

3. The identifier of the entity becomes the key of the relation

4. For associations of maximum cardinality [1:n], add the key of the

relation on the n side to the relation on the 1 side

5. For associations of maximum cardinality [n:m], a new relation should

be created using the concatenation of the keys of the associated

relations as the key. The attributes of the association should be added

as attributes of the new relation


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REMARKS

A collection of relations obtained from an entity-relation

model as described above will have the following

characteristics

Each attribute is single-value The key contains the least number of attributes

There are no dependencies between the attributes

A collection of relations that have the abovecharacteristics are considered to be of 3rd normal form


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Important problem

Some relations resulting from the translation

may not have keys

In this case, define a new key

This happens occasionally particularly in thetransformation of NM associations


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Graph of relations

Specify by pointed arrows the origin ofimported attributes

Redraw the relations in form of rectangles

Use the pointed arrows to link the relations

REMARKS

There should be no linked circle


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Practical

Model the following types of person in the

university

the students

the members of staff

Be sure to apply the methods for reducing

redundancy and guaranteeing data integrity


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Practical : University students Description

Each student has a number, a name, an address

A student is registered for a degree

A student may not register for more than one degree simultaneously

A student may take several degrees from the university

To a degree is associated a set of courses A degree is managed by a department

A course is offered by only one department

Example of questions What are the courses associated with a degree ?

What are the courses taken by a student for a degree ?

What are the courses offered by a department ?

Propose an ER model


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PracticalDocumentary information

system

A library contains the following types of document

Books

Journals that contain articles

Proceedings that contain articles

Write-ups for master and PhD works Books and write-ups are described usingtitle, authors, and a list of

keywords

Journals and proceedings are described using thetitle, editor andyear ofpublication

Articles are represented using thetitle,authors,their addresses and a listofkeywords

The authorized keywords for describing the documents are represented

using a thesaurus

Propose an ER model and the associated MR to manage the information

on the various types of document in the library as well as the thesaurus


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A thesaurus

List of concepts linked by semantic links

The semantic links are

SpecificGeneric link (hierarchy) See also (association)

Used for (synonymous)

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Example of a thesaurusTransport

Plane Boat CarVehicle

Boeing Airbus Mercedes Peugeot

Specific/generic

Specific/generic Specific/generic

See also

data modeling mit2

Documents