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2009 Pearson Education, Inc. Publishing as Prentice Hall 1

Lecture 9

Chapter 2: The Database Development Process

Modern Database Management

9thEdition

Jeffrey A. Hoffer, Mary B. Prescott,

Heikki Topi

Presentation Adapted by

Dr. Mahmoud Youssef

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Objectives Definition of terms Describe system development life cycle Explain prototyping approach

Explain agile software development approach Explain roles of individuals Explain three-schema approach Explain role of packaged data models

Explain three-tiered architectures Explain scope of database design projects Draw simple data models

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Advantages of the Database Approach I

Program-data independence Planned data redundancy (possibly to

improve efficiency)

Improved data consistency Improved data sharing Increased application development

productivity Enforcement of standards (e.g., SQL

language)

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Advantages of the Database Approach II

Improved data quality (data types which ispart of the metadata enforce whatcontents are accepted)

Improved data accessibility andresponsiveness (e.g., through indexes)

Reduced program maintenance Improved decision support

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Costs and Risks of the DatabaseApproach

New, specialized personnel

Installation and management cost and

complexity Conversion costs

Need for explicit backup and recovery

Organizational conflict

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Figure 1-5 Components of the Database Environment

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Components of theDatabase Environment

CASE Toolscomputer-aided software engineering Repositorycentralized storehouse of metadata Database Management System (DBMS)software

for managing the database Databasestorehouse of the data Application Programssoftware using the data User Interfacetext and graphical displays to users Data/Database Administratorspersonnel

responsible for maintaining the database System Developerspersonnel responsible for

designing databases and software End Userspeople who use the applications and

databases

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The Range of Database Applications

Personal databases

Workgroup databases

Departmental/divisional databases

Enterprise database

Enterprise resource planning (ERP) systems

Data warehousing implementations

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Table 1-6 Summary of Database Applications

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Figure 1-7 Workgroup database with wireless

local area network

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Enterprise Database Applications

Enterprise Resource Planning (ERP)

Integrate all enterprise functions

(manufacturing, finance, sales, marketing,inventory, accounting, human resources)

Data Warehouse

Integrated decision support system derivedfrom various operational databases

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Enterprise Data Model

First step in database development

Specifies scope and general content

Overall picture of organizational data at highlevel of abstraction

Entity-relationship diagram

Descriptions of entity types Relationships between entities

Business rules

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Figure 2-1 Segment from enterprise data model

Enterprise data model

describes the high-

level entities in an

organization and therelationship between

these entities

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Information Engineering

A data-oriented methodology to create andmaintain information systems Top-down planninga generic IS planning

methodology for obtaining a broadunderstanding of the IS needed by the entireorganization

Four steps to Top-Down planning: Planning Analysis Design Implementation

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Database Schema

External Schema User Views

Subsets of Conceptual Schema

Can be determined from business-function/data

entity matrices DBA determines schema for different users

Conceptual Schema E-R modelscovered in Chapters 3 and 4

Internal Schema Logical structurescovered in Chapter 5

Physical structurescovered in Chapter 6

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Different people

have differentviews of the

databasethese

are the external

schema

The internal

schema is the

underlying

design andimplementation

Figure 2-7 Three-schema architecture

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Pine Valley Furniture

Segment of project data model (Figure 2-11)

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Figure 2-12 Four relations (Pine Valley Furniture)

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Figure 2-12 Four relations (Pine Valley Furniture) (cont.)

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Chapter 3:Modeling Data in the

Organization

Modern Database Management

9thEditionJeffrey A. Hoffer, Mary B. Prescott,

Heikki Topi

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Objectives

Definition of terms

Importance of data modeling

Write good names and definitions for entities,relationships, and attributes

Distinguish unary, binary, and ternary relationships Model different types of attributes, entities, relationships,

and cardinalities

Draw E-R diagrams for common business situations

Convert many-to-many relationships to associativeentities

Model time-dependent data using time stamps

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Business Rules

Statements that define or constrain someaspect of the business

Assert business structure

Control/influence business behavior

Expressed in terms familiar to end users

Automated through DBMS software

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A Good Business Rule Is:

Declarativewhat, not how Preciseclear, agreed-upon meaning

Atomicone statement Consistentinternally and externally Expressiblestructured, natural language Distinctnon-redundant Business-orientedunderstood by business

people

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A Good Data Name Is:

Related to business, not technical,characteristics

Meaningful and self-documenting

Unique

Readable

Composed of words from an approved list Repeatable

Follows standard syntax

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Data Definitions

Explanation of a term or fact

Termword or phrase with specific meaning

Factassociation between two or more terms

Guidelines for good data definition

Gathered in conjunction with systems requirements

Accompanied by diagrams

Concise description of essential data meaning Achieved by consensus, and iteratively refined

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Sample E-R Diagram (Figure 3-1)