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DATA MODELING FUNDAMENTALSA Practical Guide for IT Professionals Paulraj Ponniah... DATA MODELING FUNDAMENTALSA Practical Guide for IT Professionals Paulraj Ponniah... Other Modeling Tre

DATA MODELING FUNDAMENTALS

A Practical Guide for

IT Professionals

Paulraj Ponniah

DATA MODELING FUNDAMENTALS

DATA MODELING FUNDAMENTALS

A Practical Guide for

IT Professionals

Paulraj Ponniah

Copyright # 2007 by John Wiley & Sons, Inc All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data

10 9 8 7 6 5 4 3 2 1

ToDaniel Arjun, my dear son-in-law

and toReisha and Shoba, my dear daughters-in-law

I INTRODUCTION TO DATA MODELING 1

Chapter Objectives / 3

Data Model Defined / 4

What Is a Data Model? / 5

Why Data Modeling? / 6

Who Performs Data Modeling? / 9

Information Levels / 10

Classification of Information Levels / 11

Data Models at Information Levels / 13

Conceptual Data Modeling / 17

Data Model Components / 18

Data Modeling Steps / 20

Data Model Quality / 26

Significance of Data Model Quality / 27

Data Model Characteristics / 27

Ensuring Data Model Quality / 28

Data System Development / 29

Data System Development Life Cycle / 29

Roles and Responsibilities / 33

Modeling the Information Requirements / 33

Applying Agile Modeling Principles / 34

Data Modeling Approaches and Trends / 35

Data Modeling Approaches / 36

Modeling for Data Warehouse / 38

vii

Other Modeling Trends / 39

Methods and Techniques / 47

Peter Chen (E-R) Modeling / 48

Information Engineering / 50

Integration Definition for Information Modeling / 51

Richard Barker’s Model / 53

Object-Role Modeling / 55

eXtensible Markup Language / 57

Summary and Comments / 60

Unified Modeling Language / 61

Data Modeling Using UML / 61

UML in the Development Process / 64

Chapter Summary / 68

Review Questions / 68

II DATA MODELING FUNDAMENTALS 71

Chapter Objectives / 73

Data Model Composition / 74

Models at Different Levels / 74

Conceptual Model: Review Procedure / 76

Conceptual Model: Identifying Components / 77

Identifiers / 101

Review of the Model Diagram / 103

Logical Model: Overview / 104

Identifying Entity Types / 120

Homonyms and Synonyms / 125

Category of Entity Types / 127

Exploring Dependencies / 130

Dependent or Weak Entity Types / 131

Classifying Dependencies / 132

Representation in the Model / 133

Generalization and Specialization / 134

Why Generalize or Specialize? / 136

Supertypes and Subtypes / 137

Entity Type Versus Attribute / 148

Entity Type Versus Relationship / 148

Modeling Time Dimension / 149

5 Attributes and Identifiers in Detail 157Chapter Objectives / 157

Resolution of Mixed Domains / 168

Constraints for Attributes / 169

Single-Valued and Multivalued Attributes / 171

Simple and Composite Attributes / 171

Attributes with Stored and Derived Values / 172

Optional Attributes / 173

Identifiers or Keys / 175

Need for Identifiers / 175

Definitions of Keys / 175

Guidelines for Identifiers / 176

Key in Generalization Hierarchy / 177

Attribute Validation Checklist / 178

Maximum and Minimum Cardinalities / 204

Mandatory Conditions: Both Ends / 206

Optional Condition: One End / 206

Optional Condition: Other End / 207

Optional Conditions: Both Ends / 208

Relationship or Entity Type? / 215

Ternary Relationship or Aggregation? / 216

Binary or N-ary Relationship? / 216

III DATA MODEL IMPLEMENTATION 227

Chapter Objectives / 229

Relational Model: Fundamentals / 231

Basic Concepts / 231

Structure and Components / 233

Data Integrity Constraints / 238

Transition to Database Design / 242

Design Approaches / 243

CONTENTS xi

Conceptual to Relational Model / 243

Traditional Method / 244

Evaluation of Design Methods / 245

Model Transformation Method / 246

Strengths of the Method / 277

Application of the Method / 277

Normalization Steps / 277

Fundamental Normal Forms / 278

First Normal Form / 278

Second Normal Form / 279

Third Normal Form / 281

Boyce-Codd Normal Form / 284

Higher Normal Forms / 285

Fourth Normal Form / 286

Fifth Normal Form / 287

Domain-Key Normal Form / 288

History of Decision-Support Systems / 297

Operational Versus Informational Systems / 299

System Types and Modeling Methods / 299

Data Warehouse / 301

Data Warehouse Defined / 301

Major Components / 302

Data Warehousing Applications / 305

Modeling: Special Requirements / 305

OLAP Implementation Approaches / 330

Data Modeling for OLAP / 332

Data Mining Systems / 334

Basic Concepts / 334

Data Mining Techniques / 338

Data Preparation and Modeling / 339

Data Preprocessing / 339

Data Modeling / 341

Chapter Summary / 342

Review Questions / 343

IV PRACTICAL APPROACH TO DATA MODELING 345

Chapter Objectives / 347

Significance of Quality / 348

Why Emphasize Quality? / 348

Good and Bad Models / 349

Approach to Good Modeling / 351

Checklists / 358

High-Quality Data Model / 360

Meaning of Data Model Quality / 360

Quality Dimensions / 361

What Is a High-Quality Model? / 363

Benefits of High-Quality Models / 364

Quality Assurance Process / 365

Aspects of Quality Assurance / 365

Stages of Quality Assurance Process / 366

Data Model Review / 369

Data Model Assessment / 370

Chapter Summary / 373

Review Questions / 373

Chapter Objectives / 375

The Agile Movement / 376

How It Got Started / 377

Principles of Agile Development / 378

Need for Flexibility / 386

Nature of Evolutionary Modeling / 386

Requirements: Model Interface / 400

Integration of Partial Models / 401

Conceptual Model Layout / 409

Readability and Usability / 409

Do you want to build a hybrid automobile? First, you need to create a model of the car Doyou want to build a mansion? First, you need to have blueprints and create a model of thedwelling Do you want to build a spaceship? First, you need to design a miniature model ofthe vehicle Do you want to implement a database for your organization? First, you need tocreate a data model of the information requirements

Without a proper data model of the information requirements of an enterprise, an quate database system cannot be correctly designed and implemented for the organization

ade-A good data model of high quality forms an essential prerequisite for any successful base system Unless the data modelers represent the information requirements of theorganization in a proper data model, the database design will be totally ineffective.The theme of this book is to present the fundamentals and ideas and practices aboutcreating good and useful data models—data models that can function effectively astools of communication with the user community and as database blueprints for databasepractitioners

data-THE NEED

In every industry across the board, from retail chain stores to financial institutions, frommanufacturing enterprises to government agencies, and from airline companies to utilitybusinesses, database systems have become the norm for information storage and retrieval.Whether it is a Web-based application driving electronic commerce or an inventorycontrol application managing just-in-time inventory or a data warehouse system support-ing strategic decision making, you need an effective technology to store, retrieve, and usedata in order to make the application successful It is no wonder that institutions haveadopted database technology without any reservations

In this scenario, the information technology (IT) department of every organization has aprimary responsibility to design and implement database systems and keep them running.One set of special skills for accomplishing this relates to data modeling Informationtechnology professionals with data modeling skills constitute a significant group Infor-mation technology professionals specializing in data modeling must be experts with athorough knowledge of data modeling fundamentals They must be well versed in themethodologies, techniques, and practices of data modeling

xvii

ADDRESSING THE NEED

How can IT professionals desirous of acquiring data modeling skills learn the requiredtechniques and gain proficiency in data modeling? Many seminar companies, colleges,and other teaching institutions offer courses in database design and development.However, such courses do not have data modeling as a primary focus Very fewcourses, if any, concentrate just on data modeling So, eager IT professionals are leftwith the choice of learning data modeling and gaining expert knowledge from booksexclusively on this subject How many such books should they read to learn the principlesand concepts?

This book intends to be the one definitive publication to fulfill the needs of aspiring datamodelers, of those experienced data modelers desiring to have a refresher, and even ofexpert data modelers wishing to review additional concepts In this volume, I haveattempted to present my knowledge and insights acquired through three decades of IT con-sulting, through many years of teaching data-related subjects in seminar and collegeenvironments, and through graduate and postgraduate levels of studies I do hope thisexperience will be of use to you

WHAT THIS BOOK CAN DO FOR YOU

Are you a novice data modeler? Are you fairly new to data modeling but aspire to pick upthe necessary skills? Alternatively, are you a practicing data modeler with experience inthe discipline? Are you a generalizing specialist, meaning that you want to add data mod-eling as another skill to your arsenal of IT proficiency? Irrespective of the level of yourinterest in data modeling, this is the one book that is specially designed to cover all theessentials of data modeling in a manner exactly suitable for IT professionals The booktakes a practical approach in presenting the underlying principles and fundamentals, aug-menting the presentation with numerous examples from the real world

The book begins in Part I with a broad overview of data modeling In Chapter 1, you areintroduced to all the essential concepts Before proceeding into further details, you need tofamiliarize yourself with the data modeling techniques Chapter 2 explores the leadingtechniques—the approaches, the symbols, the syntax, the semantics, and so on

Part II of the book presents the fundamentals in great detail It does not matter whatyour knowledge level of data modeling is You will find this part interesting and useful.You are presented with a real-world case study with a completed data model You areasked to study the anatomy of the data model and understand how the actual design andcreation of the data model works Part II also digs deeper into individual components of

a data model with several real-world examples

In Part III, you will learn the transition from data model to database design In recenttimes, decision-support systems have come to the forefront of computing Part IIIdescribes decision-support systems such as data warehousing and data mining andguides you through data modeling methods for these systems This is essential knowledgefor modern data modelers

In Part IV of the book, you will find a chapter exclusively devoted to quality in the datamodel Every data modeler aspires to create a model of the highest quality This chapter isrequired reading A new wave known as agile software development is on the rise

xviii PREFACE

producing great benefits You will learn about this movement and gain insights into agiledata modeling—its principles and practices.

Finally, are you looking for practical suggestions on data modeling distilled from years

of experience of many practitioners? If so, the final chapter is for you The book aptlyconcludes with such a chapter filled with numerous practical tips and suggestions

PAULRAJPONNIAH

Milltown, New Jersey

April 2007

PREFACE xix

I must also record my gratitude to the several professional colleagues who had workedwith me on various data modeling and database projects during my long IT consultingcareer Also, thanks are due to the many students in my data modeling and databaseclasses over the years Interactions with my colleagues and students have shaped thisbook in a format especially suitable for the needs of IT professionals.

xxi

INTRODUCTION TO DATA MODELING

1

DATA MODELING:

AN OVERVIEW

CHAPTER OBJECTIVES

Introduce the process of data modeling

Present why data modeling is important

Explain how a data model represents information requirements

Describe conceptual, logical, and physical data models

Briefly discuss the steps for building a data model

Show the role of data modeling in system development

Provide an initial glimpse of data modeling history and trends

James Watson and Francis Crick, working at Cambridge University, deduced thethree-dimensional structure of DNA (deoxyribonucleic acid) In 1953, they published abrief paper describing their now-famous double helix model of DNA This important mile-stone of creating a true model of DNA gave a tremendous boost to biology and genetics.For the discovery and creation of the double helix model, Watson and Crick shared theNobel Prize for Physiology and Medicine in 1962

Well, what does Watson and Crick’s achievement have to do with our current study?Essentially, they built a model The model is a true representation of the structure ofDNA—something we find in the real world Models are replicas or representations of par-ticular aspects and segments of the real world Building of models is quite common inmany disciplines When you think about it, the representation “5 þ 4¼ 9” is a mathe-matical model using symbols and logic This model represents the fact that if you putfive things together with four things of the same kind, you get nine things of the samekind In physics, we create models to represent physical properties of the world In econ-omics, we create models of economic trends and forecast economic outcomes

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Data Modeling Fundamentals By Paulraj Ponniah

Copyright # 2007 John Wiley & Sons, Inc.