Databasefundamentals Presentation

Database Database FundamentalsFundamentals


Basic & IntermediateBasic & Intermediate

©Copyright 2005, Cognizant Academy, All Rights Reserved 2

About the Author

Created By: Cognizant Academy

Version and Date:

ESSENCEOFDBASE/PPT/0604/1.0


Icons used

Questions References

Key Concepts

A Welcome Break

Demo

Brain Teasers


Module Information

Module Description

This module provides students with the basic knowledge

and skills that are needed to understand the need for

databases and how they can design them

Level Basic & Intermediate

Prerequisites Basic knowledge of data and files


Module Objective & Outline

Module Objective:After completing this Module, you would :

• Understand what is a Database System

• Know different types of Database Systems

• Be able to create a Database environment with ER Modeling

• Have a broad overview on Relational Database Management System

• Have an introduction to Structured Query Language

• Understand how the DBMS & its host computer system intercommunicate

• Be aware of the new trends in Database


Module Outline

Module Flow:

4.Structured

Query Language

2.Types of

Database Systems

3.Creating a Database

Environment

5.Internal

Management

1.What is a

Database System

6.Database

Trends


1.0 Database System

Learning Objective:

At the end of this Topic you will be able to –

• Understand what is a Database System

• Know how files are organized

• Appreciate the advantages of using a DBMS over a

traditional file system

• Be aware of the Database Architecture


What is a Database System

• A Database System is essentially a computerized

record-keeping system.

• A database-management system (DBMS) consists

of a collection of interrelated data and a set of

programs to access those data.

• Database systems are designed to manage large

volume of information.


File Organization : Terms and Concepts

• Database: Group of related files

• File: Group of records of same type

• Record: Group of related fields

• Field: Group of words or a complete number

• Byte: Group of bits that represents a single character

• Bit: Smallest unit of data; binary digit (0,1)

Data Hierarchy in a Computer System



• Entity: Person, place, thing, event about which information is maintained

• Attribute: Description of a particular entity• Key Field: Identifier field used to retrieve, update, sort a

record



• Data redundancy• Program-Data

dependence• Lack of flexibility• Poor security• Lack of data-sharing and

availability• No concurrency control

Problems with the Traditional File Environment Traditional File Processing


DBMS and its Advantages

• A Database Management System is a collection of programs

that enables users to create and maintain a database. It is a

general purpose software system that facilitates processes of

defining, constructing and manipulating databases for various

applications.• Advantages of Database approach:

• Controlling Redundancy

• Restricting Unauthorized access

• Providing persistent storage for program objects and data structures

• Permitting inference and actions using deduction rules

• Providing multiple user interface

• Representing complex relationships among data

• Enforcing integrity constraints and providing backup and recovery


• Acts as an interface

between the application

programs and physical

data files.

• Separates logical and

physical views of data

• Eliminates redundancy

of data

• Creates and maintains

databases

• Enforces data security

Database Management System (DBMS)


DBMS Architecture

• Internal Schema :

Describes physical storage structure of database

• Conceptual Schema :

Describes structure of whole database for a community of users.

• External Schema :

Each view describes that part of database that a particular user requires, and hides the rest.


DBMS Architecture

• Data Independence

Logical data independence : capacity to change

conceptual schema without having to change external

schema.

Physical data independence : capacity to change internal

schema without changing conceptual schema.


Functions of DBMS

• Data definition: Specifies content and structure of database

and defines each data element• Data manipulation: Manipulates data in a database• Data security and integrity: Monitors user requests and

rejects any unauthorized attempts• Data recovery and concurrency: Enforces certain controls

for recovery and concurrency • Data dictionary: Stores definitions of data elements, and

data characteristics• Performance: Functions should be performed efficiently


Requirements of a DBMS

Key elements in a database environment:

• Data Administration

• Data Planning and Modeling Methodology

• Database Technology and Management

• Users


Database System : Recap

• How does a database management system help

businesses improve the organization of their

information?

• Why do businesses have trouble finding the

information they need in their information systems?

• What are the advantages of using a DBMS over a

traditional file system

• State the major functions and requirements of a

DBMS


Quiz

• If a Customer Database has the following fields : EmpId, EmpName, Salary and DeptName, What would be the ideal Key field and why ?

• EmpID• EmpName• DeptName• EmpId+DeptName


2.0 Types of Databases

Learning Objective:

At the end of this Topic you will be able to explain briefly the

various types of Database Systems

• Relational DBMS

• Hierarchical DBMS

• Network DBMS

• Object-Oriented Databases


Relational Database Model

• Represents data as two-dimensional tables called relations

• Relates data across tables based on common data element Examples: DB2, Oracle, MS SQL Server


• Select: Creates subset of rows that meet specific criteria

• Join: Combines relational tables to provide users with information

• Project: Enables users to create new tables containing only relevant information

Three Basic Operations in a Relational Database


Three Basic Operations in a Relational Database

SELECT

PROJECT

JOIN


Hierarchical Database Model

• It is a pointer based model• Organizes data in a tree-like structure • Stores data in tables and views relationships as links• Supports one-to-many parent-child relationships• Prevalent in large legacy systems


Network DBMS

• Depicts data logically as many-to-many relationships• Organizes data in tables and views relationships as links• It is also a pointer based model

• Organizes data in arbitrary graphs


Hierarchical and Network DBMS

Some of the Disadvantages

• Outdated

• Complex pointer based organization

• Less flexible compared to RDBMS

• Lack support for ad-hoc and English language-like queries


Object-Oriented Databases

• Object-oriented DBMS: Stores data and procedures as objects that

can be retrieved and shared automatically

• Object-relational DBMS: Provides capabilities of both object-

oriented and relational DBMS


Types of Databases : Summary

• In a relational database the data is perceived as tables (and

nothing but tables) by the user

• The relational operators available are used to manipulate the

data in the tables


3.0 Creating a DB environment

Learning Objective:

At the end of this Topic you will –

• Have the ability to model an application system based on the E-R

Modeling approach.

• Understand the Relational Database concepts like Normalization, Data

Integrity, Relational Operations like Union, Intersection etc.

• Be able to Design Relational Databases based on E-R Models or System

Requirements for an application.


Introduction to Data Modeling

• What is Data Modeling?

A technique for analyzing requirements and for identifying the

information needs of an organization

• Why Data Modeling is important?

Cannot build a good system without knowing what data needs to be

captured and how it needs to be organized


• An Overview :

• Conceptual representation of the data structures required by a

database

• Data structures include the data objects, the associations between

data objects, and the rules which govern operations on the objects

• Focuses on what data is required and how it should be organized

• Independent of hardware or software constraints

• Data Model And Database Design:

• Data Model is to a Database what a Building plan or a blueprint is

to a Building

• A Database Design translates a data model into a database

• A Data Model is the conceptual design of a database

Introduction to Data Modeling


E-R Modeling

• Originally proposed by Peter Chen (1976)

• Views the real world as entities and relationships

• Key component is the E-R Diagram

• Most common model used for designing relational databases

• Entity- An identifiable object or concept of significance

• Attribute- Property of an entity or relationship

• Relationship- An association between entities

• Identifier- one or more attributes identifying an instance

(occurrence) of an entity


Entity relationship diagram


has works for• Dept No.

• Name

• Name• Emp Id.

Entity

Relationship Attributes

EMPLOYEEDEPARTMENT

Identifier

E-R Modeling


• Entity

• Any object or thing of significance about which data needs to be collected

and maintained

• Could be

• Concrete or tangible like a person or a building

• Abstract like a concept or activity

• Analogous to a table in a relational database

Examples: EMPLOYEES, PROJECTS, INVOICES

E-R Modeling


• Entity Rules

• Any thing or object may only be represented by one entity. Entities are

mutually exclusive in all cases.

• Each entity must be uniquely identifiable. Each instance (occurrence) of an

entity must be separate and distinctly identifiable from all other instances of

that type of entity.

• Entity Classification and Types

• Classified as dependent and independent

• An independent entity is one that does not rely on another for identification

• A dependent entity is one that relies on another for identification

• In some, methodologies, the terms used are strong and weak, respectively

E-R Modeling


• Entity Classification and Types • Fundamental entity - An entity that exists and is of interest in its own right.

Generally, most entities in the data model are fundamental entities.

Example :Department and Employee are both fundamental entities

• Special Entity Types

• Associative Entity -Used to associate two entities in order to reconcile a

many-many relationship

• Sub-type/super-type- Used in generalization hierarchies to represent a subset

of instances of their of parent entity

E-R Modeling


ORDER ITEMORDER LINEappears onfor ahas

belongs to

E-R Modeling

Example of Associative entity :


• Generalization Hierarchies

• Generalization occurs when two or more entities represent

categories of the same real-world object.

Example: CAR and TRUCK represent categories of the same

entity, VEHICLE is the super-type; CAR and TRUCK would be

the subtypes

E-R Modeling



• Form of abstraction that specifies that two or more entities that share

common attributes can be generalized into a higher level entity type

called a super-type or generic entity.

• The lower-level of entities become the sub-type, or categories, to the

super-type. Sub-types are dependent entities.

E-R Modeling



• Sub-types can be either mutually exclusive (disjoint) or

overlapping (inclusive)

• In an overlapping hierarchy an entity instance can be part of

multiple subtypes

Example: Entity PERSON represents people at a university. It has three subtypes,

FACULTY, STAFF, and STUDENT. A STAFF member could also be registered as a

STUDENT

E-R Modeling

PERSON

FACULTYSTUDENT STAFF



• In a disjoint hierarchy, an entity instance can be in only one subtype.

Example: Entity EMPLOYEE, may have two subtypes, CLASSIFIED and

WAGES. An employee may be one type or the other but not both

E-R Modeling


• Generalization Hierarchies - Nested

E-R Modeling

PERSON

FACULTYSTUDENT

UNDERGRAD GRADUATE


E-R Modeling

• Attribute

• Attributes describe a property or a characteristic of an entity

• A particular instance of an attribute is a value.

For example “John Doe” is one value of the attribute Name.

• Simple attribute• Contains only atomic values

• Composite attribute• Has component attributes

Student Name

FName

LName

MI

DOB

Simple Composite


E-R Modeling

• Attribute Classification• Single-valued attribute

• Has exactly one value per instance of an entity

• Multi-valued attribute• Contains repeating values per instance of an entity

Module

Student

Math

PhysicsId

Multi-valued

Single-valued


E-R Modeling

• Identifiers and Descriptors

• Attributes can be classified as identifiers or descriptors

• Identifiers, more commonly called keys, uniquely identify an

instance of an entity.

• A descriptor describes a non-unique characteristic of an entity

instance.

An Example :

Entity: Employee

Unique Identifier: Employee No.

Descriptor: Name, DOJ, DOB


E-R Modeling

• Relationship • Represents an association between two or more entities

Examples

- Employees work for Departments

- Departments manage one or more projects

- Employees are assigned to projects

- Projects have sub-tasks

- Orders have line items

• Defined in terms of:

- Degree

- Connectivity

- Cardinality

- Direction

- Type

- Existence


• Degree• Number of entities associated with the relationship

• Binary relationships, the association between two entities is the

• most common type in the real world. N-ary is the general form for

• degree n

• Connectivity• Mapping of associated entity instances in the relationship.

• The values of connectivity are "one" or "many”.

• Cardinality

• Actual number of related occurrences for each of the two entities.

• The basic types of connectivity for relations are: one-to-one, one-to-many, and

many-to-many.

E-R Modeling


E-R Modeling

• Connectivity and Cardinality

• A one-to-one (1:1) relationship is when at most one instance of a entity A Is

associated with one instance of entity B.

For example:

Employees in the company are each assigned their own office. For each

Employee there exists a unique office and for each office there exists a

unique employee.

• A one-to-many (1:N) relationships is when for one instance of entity A, there

are zero, one, or many instances of entity B, but for one instance of entity B,

there is only one instance of entity A.

An example :

A department has many employees each employee is assigned to one department


E-R Modeling

• Connectivity and Cardinality

• A many-to-many relationship, is when for one instance of entity A,

there are zero, one, or many instances of entity B and for one

instance of entity B there are zero, one, or many instances of entity

A.

An example is:

employees can be assigned to no more than two projects at the

same time; Project must have assigned at least three employees


• Direction• Indicates the originating entity of a binary relationship. The entity from

which a relationship originates is the parent entity; the entity where the

relationship terminates is the child entity.

• Type• The direction of a relationship is determined by its connectivity.

• Identifying and Non-identifying

• An identifying relationship is one in which one of the child entities is also

dependent entity.

• A non-identifying relationship is one in which both entities are independent.

E-R Modeling


• Existence• Denotes whether the existence of an entity instance is dependent• upon the existence of another, related, entity instance. • Defined as either mandatory or optional.

• Mandatory and optional relationship

• If an instance of an entity must always occur for an entity to be

included in a

• relationship, then it is mandatory. If the instance of the entity is not

required, it

• is optional.

Example:

Mandatory : Every project must be managed by a single department

Optional : Employees may be assigned to work on projects

E-R Modeling


• E-R Notation

• No standard notation

• Original notation by Chen

• Common notations are: Bachman, crow's foot, and IDEFIX

• All styles represent entities as rectangular boxes and relationships

as lines connecting boxes

• Each style uses a special set of symbols to represent the

cardinality of a connection

E-R Modeling


E-R Modeling

• Entities• Represented by labeled rectangles• The label is the name of the entity• Entity names should be singular nouns.

• Relationships• Represented by a solid line connecting two

entities. • Name written above the line• Relationship names should be verbs

Employee

Department

Works for


E-R Modeling

• Attributes• Listed inside the entity rectangle • Underlined • Names should be singular nouns

• Cardinality • Many is represented by a line ending in a crow's

foot. If omitted, cardinality is one

• Existence • Represented by placing a circle or a perpendicular

bar on the line • Mandatory existence is shown by the bar next to the

entity for an instance that is required• Optional existence is shown by placing a circle next

to the entity that is optional

Employee•EmpID

•EmpName


E-R Modeling : Assignment

How to create an E-R Model from Requirements ?

Step 1: Identify Entities• Entities are things people talk about, record information about and do

work on – by definition

• Any keyword (noun) is a candidate

• Identify generic object from reference to instances or occurrences

• Combine synonyms to represent a single entity

An Example : Purchase Order - System Requirements

A buyer creates a purchase order (PO) as and when the need arises. A PO is for a

Specific vendor. A PO has one or more line items. A buyer cannot create a PO of

Total value more than his approval limit. A PO can be sent to the vendor by mail,

fax, EDI. A PO can be canceled before it is submitted. A PO can be linked to a

sales order…


Step 1: Identify Entities• Entities

Purchase Order (PO)Buyer?VendorLine ItemsSales OrderApproval Limit?

• Buyer characterizes a PO• Approval Limit characterizes a Buyer

What does it tell us?

• Approval Limit is not an entity• Buyer is an entity• Approval Limit is an attribute of the entity Buyer

E-R Modeling


Step 2: Identify Relationships

Look for phrases describing a link between two things or objects

Verbs relating two nouns often suggest relationships

e.g. A buyer creates a purchase order, A purchase order has one or more

Lines

Requirements may or may not contain information regarding degree,

existence, cardinality of a relationship up front

Further questioning may need to be done to determine the above

E-R Modeling


Step 2: Identify Relationships

Grid Technique

PO

PO replaced by Buyer

Buyer creates a is approver of

Vendor

Vendor supplies against a

- - Line

Line belongs to a - created for item supplied by

-

E-R Modeling


• Step 2 : Identify Relationships• Analyzing Existing Systems (Files, Databases)

• Look for -

• Pointers

• Foreign Keys

• Repeating Groups

• Structured Codes

• All of the above imply relationships

E-R Modeling


• Step 3 : Identify Attributes

• An attribute is any detail that server to identify, classify, quantify or

express the the state of an entity

• Ask the following question for each entity “What information do you need

to know or hold about …?”

• Potential attributes are easily found by examining paper forms

E-R Modeling


• Step 3: Identify Attributes

E-R Modeling

Purchase Order No __________

Buyer _________ Vendor ___________

Date Created ______

No Item Quantity Value___ ___________ ______ _____________ ___________ ______ _____________ ___________ ______ __________

Shipping AddressStreet _________City __________Zip _______ Total Value

______

Example Purchase Order Form• Purchase Order No

• Vendor

• Buyer

• Date Created

• Item?

• Address

• City

• State

• Zip

• Total Value?


E-R Model of the Purchase Order Example

PURCHASE ORDER

VENDOR

BUYERcreates

created by

supplies against

created for a

LINE

has

belongs to

ITEM

created for

exists on

E-R Modeling


• Major Modeling Techniques• Peter Chen’s original entity/relationship diagrams• Information Engineering• Richard Barker’s notation, used by Oracle corporation• IDEF1X• Object Role Modeling• Unified Modeling Language (UML)• Extensible Markup Language (XML)

E-R Modeling


E-R Modeling

• Major Modeling Techniques

• Data Modeling has sets of two audiences:

• User community - Uses the models to verify that the analysts understand their

environment and their requirements.

• Systems designers - Use the business rules implied by the models as the basis for

their design of computer systems.

• Different techniques are better for one audience or the other.

• All techniques are fundamentally the same

• Differences are mainly in syntactic or notational


Relational Model

• Objective :

• To give an informal introduction to relational concepts especially as they

• relate to relational database design issues.

• What it is not ?

This does not give a complete description of relational theory.


Relational Model

• Formally introduced by Dr. E. F. Codd in 1970

• Represents data in the form of two-dimension tables

• A relational database is a collection of two-dimensional tables

• Basic understanding of the model needed to design and use

relational databases


• Tables, Columns and Rows

• Relationships and Keys

• Data Integrity

• Normalization

• What is a table?• Represents some real-world person, place, thing, or event

• Two-dimensional

• Columns

• Rows

Relational Model

Course No. Course_Title C_Hrs. Dept. C

CIS 120 Intro to CIS 4 Cis

MKT 333 Intro to Mkting 3 MKT

ECO 473 Labor Econ. 3 ECO

BA201 Intro to Stat. 5 ECOCIS 345 Intro to Dbase 4 CIS


• Table• Columns represent a property of the person, place, thing or event that

the table represents

• Rows represent an occurrence or instance of what the table represents

• A data value is stored in the intersection of a row and column

• Each named column has a domain, which is the set of values that may appear in that column

Relational Model

Empid Name Level DOJ Manager

101412 John M3 4/10/98 101667

102235 Nancy M4 1/23/01 101412

101398 Mike S1 8/15/95 101667

101667 Jeff M2 6/2/96 100351

103893 Cindy M3 7/17/95 101284

101116 Rahul S2 2/20/00 101412

102739 Scott C1 4/13/01 101667

Employee


Relational Model

In this document Formal Terms Many Database Manuals

Table Relation Table

Column Attribute Field

Row Tuple Record

Table - Terminology


Relational Model

• Salient features of a relational table

• Values are atomic (1NF)

• Column values are of the same kind (Domain)

• Each Row is unique (Primary Key)

• Sequence of columns is insignificant

• Sequence of rows is insignificant

• Each column must have a unique name

• Relationships and Keys• Keys - Fundamental to the concept of relational databases

• Relationship - An association between two or more tables defined by

means of keys


Relational Model

• Primary Key

• Column or a set of columns that uniquely identify a row in a table

• Must be unique and must have a value

• Foreign Key

• Column or set of columns which references the primary key or a

unique key of another table

• Rows in two tables are linked by matching the values of the foreign

key in one table with the values of the primary key in another

•EMP_ID in table EMPLOYEE is the primary key

• DEPT_NO in table DEPARTMENT is the primary key

• DEPT_NO in table EMPLOYEE is a foreign key

Examples


Relational Model

• Data Integrity

• Ensures correct and consistent navigation and manipulation of

relational tables

• Two types of integrity rules

• Entity integrity

• Referential integrity

• The entity integrity rule states that the value of the primary key can

never be a null value

• The referential integrity rule states that if a relational table has a foreign

key, then every value of the foreign key must either be null or match the

values in the relational table in which that foreign key is a primary key


Relational Model

• Data Manipulation

• Relational tables are equivalent to sets

• Operations that can be performed on sets can be performed

on relational tables

• Relational Operations such as :• Selection• Projection• Join• Union• Intersection• Difference• Product• Division

UNION

INTERSECTION

DIFFERENCE


Relational Model

• Selection• The select operator, sometimes called restrict to prevent confusion with the SQL SELECT command, retrieves subsets of rows from a relational table based on a value(s) in a column or columns

A B C D E

1 A 212 Y 2

2 C 45 N 84

3 B 8656 N 4

4 D 324 N 56

5 C 5656 Y 34

6 A 445 N 4

7 B 546 Y 55


Relational Model

• Projection• The project operator retrieves subsets of columns from a relational table removing duplicate rows from the result

A B C D E

1 A 212 Y 2

2 C 45 N 84

3 B 8656 N 4

4 D 324 N 56

5 C 5656 Y 34

6 A 445 N 4

7 B 546 Y 55


Relational Model

k x y

1 A 2

2 B 4

3 C 6

k x y

1 A 2

4 D 8

5 E 10

Table A

Table B

A TIMES B

ak ax ay bk bx by

1 A 2 1 A 2

1 A 2 4 D 8

1 A 2 5 E 10

2 B 4 1 A 2

2 B 4 4 D 8

2 B 4 5 E 10

3 C 6 1 A 2

3 C 6 4 D 8

3 C 6 5 E 10

• Product• The product of two relational tables, also called the Cartesian Product, is the concatenation of every row in one table with every row in the second. • The product of table A (having m rows) and table B (having n rows) is the table C (having m x n rows). The product is denoted as A X B or A TIMES B


Relational Model

• Join• Combines the product, selection and projection operations• Combines (concatenates) data from one row of a table with rows from

another or same table• Criteria involve a relationship among the columns in the join relational table

If the join criterion is based on equality of column value, the result is called an equi join A natural join is an equi join with redundant columns removed Joins can also be done on criteria other than equality. Such joins are called non-equi joins

k a b

1 A 2

2 B 4

3 C 6k c

1 aa

3 bb

5 cc

k a b k c

1 A 2 1 aa

3 C 6 3 bb

k a b c

1 A 2 aa

3 C 6 bb

Table A

Equi-JoinTable B

Natural Join


Relational Model

• Union• The UNION operation of two tables is formed by appending rows from one

table to those of a second to produce a third. Duplicate rows are eliminated• Tables in an UNION operation must have the same number of columns and

corresponding columns must come from the same domain

Table A

Table B

k x y

1 A 2

2 B 4

3 C 6

k x y

1 A 2

4 D 8

5 E 10

k x y

1 A 2

2 B 4

3 C 6

4 D 8

5 E 10

A Union B


Relational Model

• The UNION operation of two tables is formed by appending rows from one table

to those of a second to produce a third. Duplicate rows are eliminated• Tables in an UNION operation must have the same number of columns and

corresponding columns must come from the same domain

Table A

Table B

k x y

1 A 2

2 B 4

3 C 6

k x y

1 A 2

4 D 8

5 E 10

k x y

1 A 2

2 B 4

3 C 6

4 D 8

5 E 10

A Union B


Relational Model

• Intersection • The intersection of two relational tables is a third table that contains common rows. Both tables must be union compatible. The notation for the intersection of A and B is A [intersection] B = C or A INTERSECT B

k x y

1 A 2

2 B 4

3 C 6

k x y

1 A 2

4 D 8

5 E 10

k x y

1 A 2

Table A

Table B

A Intersect B


Relational Model

• Difference

• The difference of two relational tables is a third that contains those

rows that occur in the first table but not in the second. The Difference

operation requires that the tables be union compatible.

The notation for difference is A MINUS B or A-B. As with arithmetic, the order of

subtraction matters. That is, A - B is not the same as B - A.

k x y

1 A 2

2 B 4

3 C 6

Table A

Table B

A MINUS B

k x y

1 A 2

4 D 8

5 E 10

B MINUS A

k x y

2 B 4

3 C 6

k x y

4 D 8

5 E 10


Relational Model

Table A Table B

A DIV B

• Division• The division operator results in columns values in one table for which there are other matching column values corresponding to every row in another table.

k x y

1 A 2

1 B 4

2 A 2

3 B 4

4 B 4

3 A 2

x y

A 2

B 4

k

1

3


Normalization

Normalization theory is based on the concepts of normal forms. A relational table is said to be a particular normal form if it satisfied a certain set of constraints.

We shall discuss four normal forms in this Module.

The concept of functional dependency is the basis for the first three normal forms.

A column Y of a relational table is said to be functionally dependent upon column X

when values of column Y are uniquely identified by values of column X.

What is Functional Dependency ?

Full functional dependence applies to tables with composite keys. Column Y in relational

table R is fully functional on X of R where X is a composite key if it is functionally

dependent on X and not functionally dependent upon any subset of X.


Normalization

Un normalizedRelation

NormalizedRelation (1NF)

2 NF

3 NF

Boyce/Codd NF

Removerepeating groups

Remove partial dependencies

Remove transitive dependencies

Remove remaining Anomalies resulting from FD‘s

Remove multivalueddependencies


Normalization

An Example : A company obtains parts from a number of suppliers. Each

supplier is located in one city. A city can have more than one supplier located

there and each city has a status code associated with it. Each supplier may

provide many parts.

The company creates a simple relational table to store this information:

FIRST (s#, status, city, p#, qty)

s# Supplier identification number status Status code assigned to city City City where supplier is located p# Part number of part supplied Qty Qty of parts supplied to date

Composite primary key is (s#, p#)


Normalization

• FIRST NORMAL FORM –1NF

A relational table is said to be in the first normal form if all values of the columns are atomic. That is, they contain no repeating values.

s# city status p# qty

s1 London 20 p1 300

s1 London 20 p2 100

s1 London 20 p3 200

s1 London 20 p4 100

s2 Paris 10 p1 250

s2 Paris 10 p3 100

s3 Tokyo 30 p2 300

s3 Tokyo 30 p4 200


Normalization

• SECOND NORMAL FORM – 2NF • Table FIRST contains redundant data. Redundancy causes update

anomalies.

• Update anomalies - problems that arise when information is inserted,

deleted, or updated.

• INSERT. The fact that a certain supplier (s5) is located in a particular city

(Athens) cannot be added until they supplied a part.

• DELETE. If a row is deleted, then not only is the information about quantity and

part lost but also information about the supplier.

• UPDATE. If supplier s1 moved from London to New York, then six rows would

have to be updated with this new information.


A relational table is in second normal form 2NF if it is in 1NF and every non-key

column is fully dependent upon the primary key. That is, every non-key column

must be dependent upon the entire primary key.

FIRST is in 1NF but not in 2NF because status and city are functionally

dependent upon only on the column s# of the composite key (s#, p#).

Steps for transforming a 1NF table to 2NF is: 1. Identify any determinants other than the composite key, and the columns they

determine.

2. Create and name a new table for each determinant and the unique columns it

determines.

3. Move the determined columns from the original table to the new table.

Determinate becomes the primary key of the new table.

4. Delete the columns you just moved from the original table except for the

determinate which will serve as a foreign key.

Normalization


Normalization

SECOND NORMAL FORM – 2NF

s# city status

s1 London 20

s2 Paris 10

s3 Tokyo 30

s# p# qty

s1 p1 300

s1 p2 100

s1 p3 200

s1 p4 100

s2 p1 250

s2 p3 100

s3 p2 300

s3 p4 200

PARTS

SECOND


Normalization

• SECOND NORMAL FORM – 2NF• Modification Anomalies

• Tables in 2NF but not in 3NF still contain modification anomalies:• INSERT. The fact that a particular city has a certain status (Rome

has a status of 50) cannot be inserted until there is a supplier in the

city. • DELETE. Deleting any row in SUPPLIER destroys the status

information about the city as well as the association between

supplier and city.


Normalization

• THIRD NORMAL FORM – 2NF

A relational table is in third normal form (3NF) if it is already in 2NF and every non-key column is non transitively dependent upon its primary key.

In other words, all non-key attributes are functionally dependent only upon the primary key.

s# city status

s1 London 20

s2 Paris 10

s3 Tokyo 30

s4 Paris 10

SUPPLIERThe table supplier is in 2NF but not in 3NF because it contains a transitive dependencySUPPLIER.s# —> SUPPLIER.citySUPPLIER.city —> SUPPLIER.statusSUPPLIER.s# —> SUPPLIER.status


Normalization

• Steps for transforming a table into 3NF is:

1. Identify any determinants, other the primary key, and the columns they determine.

2. Create and name a new table for each determinant and the unique columns it determines.

3. Move the determined columns from the original table to the new table. The determinant becomes the primary key of the new table.

s# city

s1 London

s2 Paris

s3 Tokyo

s4 Paris

s5 London

SUPPLIERcity status

London 20

Paris 10

Tokyo 30

Rome 50

CITY_STATUS

The transformation of SUPPLIER into 3NF


Normalization

• Advantages of 3rd Normal form :• Eliminates redundant data which in turn saves space and reduces

manipulation anomalies.Example:

INSERT: Facts about the status of a city, Rome has a status of 50, can be added

even though there is not supplier in that city.

DELETE: Information about supplier can be deleted without destroying

information about a city.

UPDATE: Changing the location of a supplier or the status of a city requires

modifying only one row.

s# city

s1 London

s2 Paris

s3 Tokyo

s4 Paris

s5 LondonSUPPLIER

city status

London 20

Paris 10

Tokyo 30

Rome 50

CITY_STATUS

The transformation of SUPPLIER into 3NF


Normalization

• Advanced Forms :: BOYCE CODD NORMAL FORM

Many practitioners argue that placing entities in 3NF is generally

sufficient because it is rare that entities that are in 3NF are not also in

4NF and 5NF. The advanced forms of normalization are:

• Boyce-Codd Normal Form

• Fourth Normal Form

• Fifth Normal Form

Boyce-Codd normal form (BCNF) is a more rigorous version of the 3NF.

BCNF is based on the concept of determinants. A determinant column

is one on which some of the columns are fully functionally dependent.

A relational table is in BCNF if and only if every determinant is a

candidate key.


Database Design

• This section presents and discusses –• How to translate the E-R (conceptual) model (diagram) to an RDBMS

(logical) schema.

• Exercise on E-R Modeling and Database Design

• Some Guidelines - • Entities: Create one table for each simple (not a sub-type or super-

type) entity.• Attributes: Map each attribute to a candidate column with a more

precise format.• Optional attributes become null columns• Mandatory attributes become not null columns• Unique Identifier: Convert the components of the unique identifier to

the primary key of the table.


Database Design

• Sub-types: A sub-type entity is simply an entity with its own attributes or

relationships, but it also inherits any attributes and/or relationships from its parent

entity (super-type)

• 1:1 relationships: Merge the two entities into a single table, keeping all attributes.

Identify (add if needed) the primary key.

• 1:Many relationships: Create two tables, one for each entity. Post the primary key

from the 1 side to the N side (add attributes), and identify it as a foreign key. (Add the

primary key from the 1 side to the attributes on the Many side. The posted attributes

are a foreign key.)

• M:N (Many:Many) relationships: Create a new (bridge) table and post the primary

keys from both entities as attributes in the new table. The posted attributes are

foreign keys.


Database Design

A few comments… There are more rules, treating exceptions, but these are good enough in most

cases

There may occur reasons to violate the rules.

Always: use common sense and expect iterative development.

Use CASE tools like ERWin wherever possible. Tools can automatically

generate SQL table definitions from drawn E-R diagrams.


Database Design:: Assignment

Develop an E-R model and database schema for a systemto handle purchase orders.


Creating a DB environment : Summary

• The first step in designing a database application is to

understand what information the database needs to store and

what integrity constraints or business rules apply to the data.

• Data Model is to a Database what a Building plan or a blueprint

is to a Building. It is the conceptual model of the Database.

• Given a relational schema we need to decide whether it is a good

design or whether we need to decompose it into smaller

relations. Normalization gives the guidance to such

decomposition.


4.0 Structured Query Language

Learning Objectives:

At the end of this Topic you will be able to –

• Write simple SQL queries

• Get familiar with the various relational operations such as SELECT,

PROJECT and JOIN


An Introduction

SELECT column-list FROM table-names WHERE condition(s)

• Structured Query Language - (SQL) is the most widely used commercial relational database language. The SQL has several parts :

•DML – The Data Manipulation Language (DML)•DDL – The Data Definition Language (DDL)•Embedded and dynamic SQL•Security•Transaction management•Client-server execution and remote database access


• Query Processing• Query in a High Level Language (typically a 4 GL)

• Parsing : The parser converts a query, submitted by a database user and written in a high-level language, into an algebraic operators expression.

• Optimization : It is the key Topic for query processing design. It receives the expression and builds a good execution plan. The plan determines the order of execution of the operators and selects suitable algorithms for implementation of the operators.

• Code Generation for the Query : The planned code is built with the aim of retrieving the result of the query with high performance.

• Code execution by Database Processor : The query plan is executed by the execution engine Topic that delivers the result for the user.

• Result of the Query

Query Processing


Query Processing

137150

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22.506.00

SELECT column-list FROM table-names WHERE condition(s)

Conditional Selection


Query Processing

• The SQL Select Statement performs three Types of Operations

SELECT column-list FROM tables-names

WHERE condition(s)

1. Projection

3. Selection

2. Join


Course No. Course_Title C_Hrs. Dept. C

CIS 120 Intro to CIS 4 CisMKT 333 Intro to Mkting 3 MKTECO 473 Labor Econ. 3 ECOBA201 Intro to Stat. 5 ECOCIS 345 Intro to Dbase 4 CIS

Module

SELECT Module_Title, C_Hrs FROM Module

Course_Title C_Hrs.

Intro to CIS 4Intro to Mkting 3Labor Econ. 3Intro to Stat. 5Intro to Dbase 4

Result Table

Performing Projection


SELECT * FROM Module WHERE C_Hrs = 4

Course No. Course Title C. Hrs. Dept. C



ECO 473 Labor Econ. 3 ECOBA201 Intro to Stat. 5 ECOCIS 345 Intro to Dbase 4 CIS

Module

Course No. Course Title C. Hrs. Dept. C

CIS 120 Intro to CIS 4 CisCIS 345 Intro to Dbase 4 CIS

Result Table

Performing a Selection Operation


Course_No Course_Title C_ Hrs. Dept_C

CIS 120 Intro to CIS 4 CISMKT 333 Intro to Mkting 3 MKTECO 473 Labor Econ. 3 ECOBA201 Intro to Stat. 5 ECOCIS 345 Intro to Dbase 4 CIS

SELECT Module_Title, C_Hrs FROM Module WHERE Dept_C =‘CIS’

Module

Course_Title C_ Hrs.

Intro to CIS 4Intro to Dbase 4

Result Table

Performing both Projection and Selection


• Basic SELECT Statement WHERE Clause Operators

• =, <, >, <=, >=• IN (List)

• WHERE CODE IN (‘ABC’, ‘DEF’, ‘HIJ’) - would return only rows with• one of those 3 literal values for the code attribute

• BETWEEN min_val AND max_val• WHERE Qty_Ord BETWEEN 5 and 15 - would return rows where• Qty_Ord is >= 5 and <= 15 - Works on character data using ascending

alphabetical order• LIKE “literal with wildcards” % used for multiple chars. _ single char.

• WHERE Name LIKE ‘_o%son’ - returns rows where name has o as the 2nd character and ends with son - Torgeson or Johnson

• NOT• WHERE NOT Name = ‘Johnson’ - would return all rows where name <> Johnson

- lowest priority in operator order • AND and OR, Use Parentheses to control order

Performing both Projection and Selection


• Joining Tables

• To appropriately join tables, the tables must be related and we apply a

where clause which equates the primary key column of the table on

the one side of the relationship with the parallel foreign key column of

the many side table.

• This type of join is called an Equi-join.

• Our example will join Modules and departments where dept_code is the

linking “key” column.

• The next series of slides takes you through a step by step process of

combining data rows from one table with data rows in another table.

• The first slide introduces the SQL Select statement the shows the join

operation and a picture of the two tables that the join will operate on.

• The next slides show progressive steps in the join process.

Joining Tables


Joining Two Tables - Select and Tables

Course_No Course_Title C_Hrs Dept_Code





Dept Code Dept name Office#MKT Marketing 244CIS Comp. Info. Sys. 302ECO Economics 244

Module

Department

SELECT * FROM Module C, department D WHERE D.Dept_Code = C.Dept_Code

SQL will compare every row of the1st table with the first row of the 2ndtable. Then it will compare all rows of

the 1st with the second row of the second, and so on only rows where the condition

is met are placed in the result table.

Joining Tables


Joining Two Tables - Row 1 Module to Row 1 Dept


CIS 120 Intro to CIS 4 CIS



BA201 Intro to Stat. 5 ECOCIS 345 Intro to Dbase 4 CIS Dept Code Dept name Office#

MKT Marketing 244CIS Comp. Info. Sys. 302ECO Economics 244

ModuleDepartment


Course_No Course_Title C_Hrs Dept_Code Dept_Name Office#

RESULT TABLE

No match so row notplaced in results

Joining Tables







BA201 Intro to Stat. 5 ECOCIS 345 Intro to Dbase 4 CIS Dept Code Dept name Office#

MKT Marketing 244CIS Comp. Info. Sys. 302ECO Economics 244

ModuleDepartment



CIS 120 Intro to CIS 4 Cis Comp. Info S 302

RESULT TABLE

Match on conditioncauses a result row tobe produced.

Joining Tables








Dept Code Dept name Office#MKT Marketing 244CIS Comp. Info. Sys. 302ECO Economics 244

Module

Department



CIS 120 Intro to CIS 4 Cis Comp. Info S 302

RESULT TABLE

Joining Tables


5.0 Internal Management

Learning Objective

– After completing this topic you will be able to :

• Describe the various components of the computer system that

provide data storage facilities to a DBMS

• Understand how DBMS communicates with the host system

• Outline some of the database tuning factors


Computer file management and DBMS

• Computer files are stored in external media such as disks and tapes.• Direct access

• Sequential access

• Input output of data and memory management is managed by the Operating system • File manager

• Disk manager DBMS

File Request

File Manager

Logical Page Req Disk Manager

Physical Page Access

DBMS/Host inter-com


Intercommunication

• DBMS/Host communication :

• A file is a collection of pages. A page is a unit of Input Output.

• The DBMS sends a file request to the file manager.

• The file manager has no idea where the requested page is physically stored.

• The file manager in turn communicates with the disk manager.

• The file manager provides the database system with the given page.

• The database system converts the same into a logical form as

understandable by the user.


Tuning at the internal level

• Indexes• Database indexes are important means of speeding up access to set of

records. Especially in a relational database.

• Index is very useful in existence tests.

• Once a index is created it is transparent to the user.

• Hashing• Hashing is directly determining a page address for a given record without the

overhead of creating indexes.

• The main problem associated with hashing are overflow & underflow.

• Clusters• Physically storing related pages in the form of intra file subsets.

• Inter file clustering to store records from distributed databases in the same physical page.


Internal Management : Summary

• Database files are stored in logical page sets.

• The underlying physical files that store a database need not map to

the logical representation of the DBMS.

• Indexes are useful means of speeding up data access in large

databases . They incur overheads.

• Hashed functions speed up individual record access, however has

overflow & underflow problems.

• Intra and inter file clustering of the physical records speed up

certain operations at the cost of other types of data manipulations.


6.0 Database Trends

Learning Objective

– At the end of this Topic you will be :

• Familiar with various terms like

• OLAP

• Data warehousing

• Data mining

• Aware of the business needs that require data to be analyzed in

multiple dimensions


Multidimensional Data Analysis

• On-line analytical processing (OLAP)

• Multidimensional data analysis

• Supports manipulation and analysis of large volumes of data from

multiple dimensions/perspectives


• Major Types of Databases

cen tra lised d a tab ases d is trib u ted d a tab ases n e tw ork d a tab ases

D atab ases

Types of databases


• Used by single central processor or multiple processors in client/server network

CPU Disk Controller Printer Controller

Tape driveController

Memory Controller

Memory

disk printerTape Drive

System bus

Centralized database


• Stored in more than one physical location• Partitioned database • Duplicated database

Distributed database


• On-line analytical processing (OLAP)• Multidimensional data analysis

• Supports manipulation and analysis of large volumes of data from multiple

dimensions/perspectives

Multidimensional data model


• Supports reporting and query tools

• Stores current and historical data

• Consolidates data for management analysis and decision making

Data warehouse


• Data mart

• Subset of data warehouse

• Contains summarized or highly focused portion of data for a specified

function or group of users

• Data mining

• Tools for analyzing large pools of data

• Find hidden patterns and infer rules to predict trends

Data warehouse


• Hypermedia database• Organizes data as network of nodes

• Links nodes in pattern specified by user

• Supports text, graphic, sound, video and executable programs

Databases and the web


• Database server • Computer in a client/server environment runs a DBMS to process SQL

statements and perform database management tasks

• Application server

• Software handling all application operations

Databases and the web


Database Trends : Summary

• The database forms the backend for any kind of application

architecture be it a client server, distributed system such as the

web etc.

• Users want to see data in as many dimensions possible, therefore it

is important to be aware of concepts regarding Data warehousing ,

Data mining and On-line analytical processing (OLAP)


Database Fundamentals: Next Step

Resource Type

Description Reference Topic or Topic

Book Case*Method: Entity Relationship Modeling - Richard Barker

Book Data & Databases – Joe Celko

Book An Introduction to Database Systems – C. J. Date

Book The Data Modeling Handbook - Reingruber and Gregory

Book Data Modeling for Information Professionals – Bob Schmidt

Book Data Model Patterns – David C. Hay, Richard Barker

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