oracle

Database Fundamentals
Basic & Intermediate

Module Objective:
After completing this Module, you should :


Understand what is a Database System



Explain briefly 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

1.

2.

What is a
Database System

Types of
Database Systems

5.

6.

Internal
Management

Database
Trends

3.

4.

Creating a
Database
Environment

Structured
Query
Language

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

Concepts


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

Concepts
Problems with the Traditional File Environment


Data redundancy



Program-Data dependence



Lack of flexibility



Poor security



Lack of data-sharing and
availability



No concurrency control

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
• Providing backup and recovery

Database Management System
(DBMS)


Acts as an interface between
application programs and physical
data files.



Separates logical and physical
views of data



Eliminates redundancy of data



Creates and maintains databases



Enforces security of data

Figure 7-4

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 :
•

•

Data manipulation :
•

•

Enforces certain controls for recovery and concurrency

Data dictionary:
•

•

Monitors user requests and rejects any unauthorized attempts

Data recovery and concurrency :
•

•

Manipulates data in a database

Data security and integrity :
•

•

Specifies content and structure of database and defines each data
element

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
•

Why do businesses have trouble finding the information
they need in their information systems?

•

How does a database management system help businesses
improve the organization of their information?

•

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

Three Basic Operations in a
Relational Database
•

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

JOIN

SELECT
PROJECT

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

Introduction to Data Modeling
•

An Overview :
•

•

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

•
•

Conceptual representation of the data structures required by
a database

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

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

E-R Modeling
Entity

has
DEPARTMENT
• Dept No.
• Name

Identifier

Relationship

works for

Attributes

EMPLOYEE
• Name
• Emp Id.

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
Example of Associative entity :

ORDER

has

belongs to

ORDER LINE

for a
appears on

ITEM

E-R Modeling
•

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
• Generalization Hierarchies
•

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
• Generalization Hierarchies
•

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

PERSON

STUDENT

STAFF

FACULTY

E-R Modeling
•

Generalization Hierarchies
•

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

PERSON

STUDENT

UNDERGRAD

FACULTY

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

FName
MI
Student

Name

DOB
Simple

LName
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

Multi-valued
Singlevalued

Math
Module

Id

Student

Physics

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

E-R Modeling
•

Degree
•
•

Binary relationships, the association between two entities is the

•

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

•

•

Number of entities associated with the relationship

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 manyto-many.

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

E-R Modeling
•

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
• 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
Works for

Department

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…

E-R Modeling
Step 1: Identify Entities
• Entities
Purchase Order (PO)
Buyer?
Vendor
Line Items
Sales Order
Approval 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 Vendor
of

Vendor

supplies
against a

-

-

Line

belongs to a

-

created for
item supplied
by

Line
-

E-R Modeling


Step 2 : Identify Relationships
•

Analyzing Existing Systems (Files, Databases)

•

Look for 


Foreign Keys



Repeating Groups


•

Pointers

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

Example Purchase Order Form

Purchase Order No __________
Buyer _________ Vendor ___________
Date Created ______
No Item
Quantity Value
___ ___________ ______ __________
___ ___________ ______ __________
___ ___________ ______ __________
Shipping Address
Street _________
City __________
Total Value ______
Zip _______

• Purchase Order No
• Vendor
• Buyer
• Date Created
• Item?
• Address
• City
• State

• Zip
• Total Value?

E-R Modeling
E-R Model of the Purchase Order Example
creates
BUYER

created by

created for a
PURCHASE
ORDER

has

supplies against
belongs to

exists on
ITEM

LINE

created for

VENDOR

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
•

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

Relational Model


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

Course No.

Course_Title

C_Hrs. Dept. C

CIS 120

Intro to CIS

4 Cis

MKT 333

Intro to Mkting

3 MKT

ECO 473
BA201
CIS 345

Labor Econ.
Intro to Stat.
Intro to Dbase

3 ECO
5 ECO
4 CIS

Relational Model


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
Empid

Level

DOJ

Manager

101412

Employee

Name
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

Relational Model
Table - Terminology

In this
document

Formal Terms

Many Database
Manuals

Table

Relation

Table

Column

Attribute

Field

Row

Tuple

Record

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

INTERSECTION
UNION

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
• 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

ak ax

ay

bk bx

by

y

1

A

2

1

A

2

1

A

2

1

A

2

4

D

8

B

4

1

A

2

5

E

10

3

C

6

2

B

4

1

A

2

k
Table B

x

2

Table A

k

x

y

2

B

4

4

D

8

1

A

2

2

B

4

5

E

10

4

D

8

3

C

6

1

A

2

5

E

10

3

C

6

4

D

8

3

C

6

5

E

10

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

k

a

1

A

2

Table B

c

Equi-Join

2

B

4

k

3

C

6

1

bb

5

cc

b k

c

1

A

2 1

aa

C

6 3

bb

aa

3

a

3

b

Table A

Natural Join

k

a

b c

1

A

2 aa

3

C

6 bb

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
A Union B

k

k

x

1

A

2

2

B

4

3

C

6

x

y

1
Table A

k

A

2

4

D

8

5

E

10

A

2

2

Table B

y

1

y

x
B

4

3

C

6

4

D

8

5

E

10

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
A Union B

k

x

y

k

x

y

1

A

2

1

A

2

2

B

4

2

B

4

3

C

6

4

D

8

5

E

10

3 Table A 6
C

Table B

k

x

y

1

A

2

4

D

8

5

E

10

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

A Intersect B

y

1

A

2

Table B

y

A

2

D

8

5

x

x

4

k

k
1

Table A

E

10

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

k

x

y

1

A

2

4

D

8

5

E

10

A MINUS B

Table B

y

B

4

3
B MINUS A

x

2

Table A

k

C

6

k

x

y

4

D

8

5

E

10

Relational Model

• 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

k

1

A

2

1

1

B

4

3

2

A

2

x

y

3

B

4

A

2

4

B

4

B

3

A
Table A

2

A DIV B

4
Table B

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.

What is Functional Dependency ?
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.

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 normalized
Relation

Remove
repeating groups

Normalized
Relation (1NF)
Remove partial
dependencies
2 NF
Remove transitive
dependencies
3 NF

Remove remaining
Anomalies resulting
from FD„s

Boyce/Codd NF
Remove multivalued
dependencies

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#
status
City
p#
Qty

Supplier identification number
Status code assigned to city
City where supplier is located
Part number of part supplied
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.

Normalization
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
• 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
SECOND NORMAL FORM – 2NF
PARTS

s#

p#

qty

s1

p1

300

s1

p2

100

SECOND

s1

p3

200

s#

s1

p4

100

s1

London

20

s2

p1

250

s2

Paris

10

s2

p3

100

s3

Tokyo

30

s3

p2

300

s3

p4

200

city

status

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.

SUPPLIER

s#

city

status

s1

London

20

s2

Paris

10

s3

Tokyo

30

s4

Paris

10

The table supplier is in 2NF but not in
3NF because it contains a transitive
dependency
SUPPLIER.s# —> SUPPLIER.city
SUPPLIER.city —>
SUPPLIER.status
SUPPLIER.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.
SUPPLIER

s#

CITY_STATUS

city

s1
The transformation of
SUPPLIER into 3NF

city

status

London

London

20

s2

Paris

Paris

10

s3

Tokyo

Tokyo

30

s4

Paris

Rome

50

s5

London

•

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

CITY_STATUS

city

status

s1
s2
SUPPLIER

Paris

London

20

s3

The transformation of
SUPPLIER into 3NF

London
Tokyo

Paris

10

s4

Paris

Tokyo

30

s5

London

Rome

50

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
system
to 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
• 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

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

Query Processing


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

Conditional Selection
137
150

Door latch
Door seal

22.50
6.00

Query Processing
• The SQL Select Statement performs three Types of Operations

1. Projection

SELECT column-list FROM tables-names
WHERE condition(s)
2. Join
3. Selection

Performing Projection
SELECT Module_Title, C_Hrs FROM Module

Module

Result Table

Cours e No.

Cours e_Title

C_Hrs . Dept. C

CIS 120

Intro to CIS

4 Cis

Intro to C IS

4

M K T 333

Intro to M k ting

3 MKT

Intro to M k ting

3

E CO 473
B A 201
CIS 345

Labor E c on.
Intro to S tat.
Intro to Dbas e

3 E CO
5 E CO
4 CIS

Labor E c on.
Intro to S tat.
Intro to D bas e

3
5
4

C ours e_Title

C _H rs .

Performing a Selection Operation
SELECT * FROM Module WHERE C_Hrs = 4

Course No.

Course Title

CIS 120
MKT 333
ECO 473
BA201
CIS 345

Intro to CIS
Intro to Mkting
Labor Econ.
Intro to Stat.
Intro to Dbase

Course No.

Course Title

CIS 120
CIS 345

Intro to CIS
Intro to Dbase

C. Hrs. Dept. C
4
3
3
5
4

Module

Cis
MKT
ECO
ECO
CIS

C. Hrs. Dept. C
4 Cis
4 CIS

Result Table

Performing both Projection and Selection
SELECT Module_Title, C_Hrs FROM Module WHERE Dept_C =„CIS‟

Result Table

Module
C ours e_N o

C ours e_Title

C _ H rs . D ept_C

C IS 120

Intro to C IS

4 C IS

M K T 333

Intro to M k ting

3 MKT

E C O 473
B A 201
C IS 345

Labor E c on.
Intro to S tat.
Intro to D bas e

3 E CO
5 E CO
4 C IS

Cours e_Title
Intro to CIS
Intro to Dbas e

C_ Hrs .
4
4

Performing both Projection and Selection
• Basic SELECT Statement WHERE Clause Operators
•
•

=, <, >, <=, >=
IN (List)
•
•

•

BETWEEN min_val AND max_val
•
•

•

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

Joining Tables


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 next slides show progressive steps in the join process.

•

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.

Joining Tables
Joining Two Tables - Select and Tables
SELECT * FROM Module C, department D WHERE D.Dept_Code = C.Dept_Code

Module
Course_No

Course_Title

C_Hrs Dept_Code

CIS 120

Intro to CIS

4 Cis

MKT 333

Intro to Mkting

3 MKT

ECO 473
BA201
CIS 345

Labor Econ.
Intro to Stat.
Intro to Dbase

3 ECO
5 ECO
4 CIS

Department
SQL will compare every row of the
1st table with the first row of the 2nd
table. 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.

D e p t C o d e D e p t nam e

O ffic e #

MK T

M arke ting

244

C IS

C o m p . Info . S ys .

302

ECO

E c o no m ic s

244

Joining Tables
Joining Two Tables - Row 1 Module to Row 1 Dept

Course_No
CIS 120

Intro to CIS

4 CIS

MKT 333

Module

Course_Title

C_Hrs Dept_Code

Intro to Mkting

3 MKT

ECO 473
BA201
CIS 345

Labor Econ.
Intro to Stat.
Intro to Dbase

3 ECO
5 ECO
4 CIS

No match so row not
placed in results

Department
Dept Code
MKT
CIS
ECO

Dept name
Office#
Marketing
244
Comp. Info. Sys.
302
Economics
244

RESULT TABLE
Course_No

Course_Title

C_Hrs Dept_Code

Dept_Nam e Office#

Joining Tables
Course_No
CIS 120

Intro to CIS

4 Cis

MKT 333

Module

Course_Title

C_Hrs Dept_Code

Intro to Mkting

3 MKT

ECO 473
BA201
CIS 345

Labor Econ.
Intro to Stat.
Intro to Dbase

3 ECO
5 ECO
4 CIS

Match on condition
causes a result row to
be produced.

Department
Dept Code
MKT
CIS
ECO

Dept name
Office#
Marketing
244
Comp. Info. Sys.
302
Economics
244

RESULT TABLE
Course_No

Course_Title

CIS 120

Intro to CIS

C_Hrs Dept_Code Dept_Name Office#
4 Cis

Comp. Info S

302

Joining Tables
Course_No
CIS 120

Intro to CIS

4 Cis

MKT 333

Module

Course_Title

C_Hrs Dept_Code

Intro to Mkting

3 MKT

ECO 473
BA201
CIS 345

Labor Econ.
Intro to Stat.
Intro to Dbase

3 ECO
5 ECO
4 CIS

Department
Dept Code
MKT
CIS
ECO

Dept name
Office#
Marketing
244
Comp. Info. Sys.
302
Economics
244

RESULT TABLE
Course_No

Course_Title

CIS 120

Intro to CIS

C_Hrs Dept_Code Dept_Name Office#
4 Cis

Comp. Info S

302

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
DBMS
• Disk manager
File Request

DBMS/Host inter-com

File Manager

Logical
Page Req

Disk Manager

Physical
Page Access

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
•

•
•


Hashing
•

•



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 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

Types of databases
• Major Types of Databases

Databases

centralis ed databases dis trib uted databases

network databases

Centralized database


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

CPU

printer

Disk Controller

Printer
Controller

Tape Drive

Tape drive
Controller

System bus
Memory Controller
Memory

Distributed database


Stored in more than one physical location
•
•

Partitioned database
Duplicated database

Multidimensional data model


On-line analytical processing (OLAP)
•

Multidimensional data analysis

•

Supports manipulation and analysis of large volumes
of data from multiple dimensions/perspectives

Data warehouse


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

Databases and the web


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

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

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 Rein Gruber and Gregory

Book

Data Modeling for Information
Professionals – Bob Schmidt

Book

Data Model Patterns – David C.
Hay, Richard Barker

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