Data Modeling Fundamentals A Practical Guide for IT Professionals

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

ISBN: 9780471790495 | 0471790494
Cover: Hardcover
Copyright: 7/20/2007

Buy New

Print on Demand: 2-4 Weeks. This item cannot be cancelled or returned.

$182.76

The purpose of this book is to provide a practical approach for IT professionals to acquire the necessary knowledge and expertise in data modeling to function effectively. It begins with an overview of basic data modeling concepts, introduces the methods and techniques, provides a comprehensive case study to present the details of the data model components, covers the implementation of the data model with emphasis on quality components, and concludes with a presentation of a realistic approach to data modeling. It clearly describes how a generic data model is created to represent truly the enterprise information requirements.

Preface	p. xvii
Acknowledgments	p. xxi
Introduction to Data Modeling	p. 1
Data Modeling: An Overview	p. 3
Chapter Objectives	p. 3
Data Model Defined	p. 4
What Is a Data Model?	p. 5
Why Data Modeling?	p. 6
Who Performs Data Modeling?	p. 9
Information Levels	p. 10
Classification of Information Levels	p. 11
Data Models at Information Levels	p. 13
Conceptual Data Modeling	p. 17
Data Model Components	p. 18
Data Modeling Steps	p. 20
Data Model Quality	p. 26
Significance of Data Model Quality	p. 27
Data Model Characteristics	p. 27
Ensuring Data Model Quality	p. 28
Data System Development	p. 29
Data System Development Life Cycle	p. 29
Roles and Responsibilities	p. 33
Modeling the Information Requirements	p. 33
Applying Agile Modeling Principles	p. 34
Data Modeling Approaches and Trends	p. 35
Data Modeling Approaches	p. 36
Modeling for Data Warehouse	p. 38
Other Modeling Trends	p. 39
Chapter Summary	p. 41
Review Questions	p. 41
Methods, Techniques, and Symbols	p. 43
Chapter Objectives	p. 43
Data Modeling Approaches	p. 44
Semantic Modeling	p. 44
Relational Modeling	p. 45
Entity-Relationship Modeling	p. 46
Binary Modeling	p. 46
Methods and Techniques	p. 47
Peter Chen (E-R) Modeling	p. 48
Information Engineering	p. 50
Integration Definition for Information Modeling	p. 51
Richard Barker's Model	p. 53
Object-Role Modeling	p. 55
eXtensible Markup Language	p. 57
Summary and Comments	p. 60
Unified Modeling Language	p. 61
Data Modeling Using UML	p. 61
UML in the Development Process	p. 64
Chapter Summary	p. 68
Review Questions	p. 68
Data Modeling Fundamentals	p. 71
Anatomy of a Data Model	p. 73
Chapter Objectives	p. 73
Data Model Composition	p. 74
Models at Different Levels	p. 74
Conceptual Model: Review Procedure	p. 76
Conceptual Model: Identifying Components	p. 77
Case Study	p. 81
Description	p. 81
E-R Model	p. 84
UML Model	p. 87
Creation of Models	p. 89
User Views	p. 90
View Integration	p. 92
Entity Types	p. 96
Specialization/Generalization	p. 98
Relationships	p. 98
Attributes	p. 100
Identifiers	p. 101
Review of the Model Diagram	p. 103
Logical Model: Overview	p. 104
Model Components	p. 104
Transformation Steps	p. 107
Relational Model	p. 109
Physical Model: Overview	p. 111
Model Components	p. 111
Transformation Steps	p. 112
Chapter Summary	p. 113
Review Questions	p. 113
Objects or Entities in Detail	p. 115
Chapter Objectives	p. 115
Entity Types or Object Sets	p. 116
Comprehensive Definition	p. 116
Identifying Entity Types	p. 120
Homonyms and Synonyms	p. 125
Category of Entity Types	p. 127
Exploring Dependencies	p. 130
Dependent or Weak Entity Types	p. 131
Classifying Dependencies	p. 132
Representation in the Model	p. 133
Generalization and Specialization	p. 134
Why Generalize or Specialize?	p. 136
Supertypes and Subtypes	p. 137
Generalization Hierarchy	p. 138
Inheritance of Attributes	p. 140
Inheritance of Relationships	p. 140
Constraints	p. 141
Rules Summarized	p. 144
Special Cases and Exceptions	p. 144
Recursive Structures	p. 145
Conceptual and Physical	p. 145
Assembly Structures	p. 147
Entity Type Versus Attribute	p. 148
Entity Type Versus Relationship	p. 148
Modeling Time Dimension	p. 149
Categorization	p. 150
Entity Validation Checklist	p. 153
Completeness	p. 153
Correctness	p. 154
Chapter Summary	p. 155
Review Questions	p. 155
Attributes and Identifiers in Detail	p. 157
Chapter Objectives	p. 157
Attributes	p. 158
Properties or Characteristics	p. 158
Attributes as Data	p. 161
Attribute Values	p. 162
Names and Descriptions	p. 163
Attribute Domains	p. 164
Definition of a Domain	p. 164
Domain Information	p. 165
Attribute Values and Domains	p. 166
Split Domains	p. 167
Misrepresented Domains	p. 167
Resolution of Mixed Domains	p. 168
Constraints for Attributes	p. 169
Value Set	p. 169
Range	p. 170
Type	p. 170
Null Values	p. 170
Types of Attributes	p. 171
Single-Valued and Multivalued Attributes	p. 171
Simple and Composite Attributes	p. 171
Attributes with Stored and Derived Values	p. 172
Optional Attributes	p. 173
Identifiers or Keys	p. 175
Need for Identifiers	p. 175
Definitions of Keys	p. 175
Guidelines for Identifiers	p. 176
Key in Generalization Hierarchy	p. 177
Attribute Validation Checklist	p. 178
Completeness	p. 178
Correctness	p. 179
Chapter Summary	p. 180
Review Questions	p. 180
Relationships in Detail	p. 183
Chapter Objectives	p. 183
Relationships	p. 184
Associations	p. 184
Relationship: Two-Sided	p. 186
Relationship Sets	p. 187
Double Relationships	p. 187
Relationship Attributes	p. 189
Degree of Relationships	p. 190
Unary Relationship	p. 191
Binary Relationship	p. 191
Ternary Relationship	p. 193
Quaternary Relationship	p. 193
Structural Constraints	p. 194
Cardinality Constraint	p. 195
Participation Constraint	p. 198
Dependencies	p. 200
Entity Existence	p. 200
Relationship Types	p. 201
Identifying Relationship	p. 202
Nonidentifying Relationship	p. 204
Maximum and Minimum Cardinalities	p. 204
Mandatory Conditions: Both Ends	p. 206
Optional Condition: One End	p. 206
Optional Condition: Other End	p. 207
Optional Conditions: Both Ends	p. 208
Special Cases	p. 209
Gerund	p. 209
Aggregation	p. 210
Access Pathways	p. 211
Design Issues	p. 215
Relationship or Entity Type?	p. 215
Ternary Relationship or Aggregation?	p. 216
Binary or N-ary Relationship?	p. 216
One-to-One Relationships	p. 217
One-to-Many Relationships	p. 219
Circular Structures	p. 219
Redundant Relationships	p. 221
Multiple Relationships	p. 221
Relationship Validation Checklist	p. 222
Completeness	p. 223
Correctness	p. 224
Chapter Summary	p. 225
Review Questions	p. 225
Data Model Implementation	p. 227
Data Modeling to Database Design	p. 229
Chapter Objectives	p. 229
Relational Model: Fundamentals	p. 231
Basic Concepts	p. 231
Structure and Components	p. 233
Data Integrity Constraints	p. 238
Transition to Database Design	p. 242
Design Approaches	p. 243
Conceptual to Relational Model	p. 243
Traditional Method	p. 244
Evaluation of Design Methods	p. 245
Model Transformation Method	p. 246
The Approach	p. 246
Mapping of Components	p. 249
Entity Types to Relations	p. 250
Attributes to Columns	p. 250
Identifiers to Keys	p. 252
Transformation of Relationships	p. 252
Transformation Summary	p. 267
Chapter Summary	p. 269
Review Questions	p. 269
Data Normalization	p. 271
Chapter Objectives	p. 271
Informal Design	p. 272
Forming Relations from Requirements	p. 272
Potential Problems	p. 273
Update Anomaly	p. 275
Deletion Anomaly	p. 275
Addition Anomaly	p. 276
Normalization Methodology	p. 276
Strengths of the Method	p. 277
Application of the Method	p. 277
Normalization Steps	p. 277
Fundamental Normal Forms	p. 278
First Normal Form	p. 278
Second Normal Form	p. 279
Third Normal Form	p. 281
Boyce-Codd Normal Form	p. 284
Higher Normal Forms	p. 285
Fourth Normal Form	p. 286
Fifth Normal Form	p. 287
Domain-Key Normal Form	p. 288
Normalization Summary	p. 290
Review of the Steps	p. 290
Normalization as Verification	p. 291
Chapter Summary	p. 292
Review Questions	p. 292
Modeling for Decision-Support Systems	p. 295
Chapter Objectives	p. 295
Decision-Support Systems	p. 296
Need for Strategic Information	p. 296
History of Decision-Support Systems	p. 297
Operational Versus Informational Systems	p. 299
System Types and Modeling Methods	p. 299
Data Warehouse	p. 301
Data Warehouse Defined	p. 301
Major Components	p. 302
Data Warehousing Applications	p. 305
Modeling: Special Requirements	p. 305
Dimensional Modeling	p. 308
Dimensional Modeling Basics	p. 309
STAR Schema	p. 312
Snowflake Schema	p. 318
Families of STARS	p. 321
Transition to Logical Model	p. 322
OLAP Systems	p. 325
Features and Functions of OLAP	p. 325
Dimensional Analysis	p. 326
Hypercubes	p. 328
OLAP Implementation Approaches	p. 330
Data Modeling for OLAP	p. 332
Data Mining Systems	p. 334
Basic Concepts	p. 334
Data Mining Techniques	p. 338
Data Preparation and Modeling	p. 339
Data Preprocessing	p. 339
Data Modeling	p. 341
Chapter Summary	p. 342
Review Questions	p. 343
Practical Approach to Data Modeling	p. 345
Ensuring Quality in the Data Model	p. 347
Chapter Objectives	p. 347
Significance of Quality	p. 348
Why Emphasize Quality?	p. 348
Good and Bad Models	p. 349
Approach to Good Modeling	p. 351
Quality of Definitions	p. 351
Importance of Definitions	p. 352
Aspects of Quality Definitions	p. 353
Correctness	p. 353
Completeness	p. 354
Clearness	p. 357
Format	p. 358
Checklists	p. 358
High-Quality Data Model	p. 360
Meaning of Data Model Quality	p. 360
Quality Dimensions	p. 361
What Is a High-Quality Model?	p. 363
Benefits of High-Quality Models	p. 364
Quality Assurance Process	p. 365
Aspects of Quality Assurance	p. 365
Stages of Quality Assurance Process	p. 366
Data Model Review	p. 369
Data Model Assessment	p. 370
Chapter Summary	p. 373
Review Questions	p. 373
Agile Data Modeling in Practice	p. 375
Chapter Objectives	p. 375
The Agile Movement	p. 376
How It Got Started	p. 377
Principles of Agile Development	p. 378
Philosophies	p. 378
Generalizing Specialists	p. 379
Agile Modeling	p. 379
What Is Agile Modeling?	p. 380
Basic Principles	p. 380
Auxiliary Principles	p. 381
Practicing Agile Modeling	p. 381
Primary Practices	p. 381
Additional Practices	p. 382
Role of Agile DBA	p. 383
Agile Documentation	p. 383
Recognizing an Agile Model	p. 384
Feasibility	p. 384
Evolutionary Data Modeling	p. 385
Traditional Approach	p. 385
Need for Flexibility	p. 386
Nature of Evolutionary Modeling	p. 386
Benefits	p. 387
Chapter Summary	p. 388
Review Questions	p. 388
Data Modeling: Practical Tips	p. 391
Chapter Objectives	p. 391
Tips and Suggestions	p. 392
Nature of Tips	p. 392
How Specified	p. 392
How to Use Them	p. 392
Requirements Definition	p. 393
Interviews	p. 393
Group Sessions	p. 394
Geographically Dispersed Groups	p. 394
Documentation	p. 395
Change Management	p. 395
Notes for Modeling	p. 396
Stakeholder Participation	p. 396
Organizing Participation	p. 397
User Liaison	p. 397
Continuous Interaction	p. 398
Multiple Sites	p. 399
Iterative Modeling	p. 399
Establishing Cycles	p. 399
Determining Increments	p. 400
Requirements: Model Interface	p. 400
Integration of Partial Models	p. 401
Special Cases	p. 401
Legal Entities	p. 402
Locations and Places	p. 403
Time Periods	p. 405
Persons	p. 407
Bill-of-Materials	p. 409
Conceptual Model Layout	p. 409
Readability and Usability	p. 409
Component Arrangement	p. 410
Adding Texts	p. 416
Visual Highlights	p. 417
Logical Data Model	p. 417
Enhancement Motivation	p. 418
Easier Database Implementation	p. 418
Performance Improvement	p. 418
Storage Management	p. 419
Enhanced Representation	p. 419
Chapter Summary	p. 421
Review Questions	p. 421
Bibliography	p. 423
Glossary	p. 425
Index	p. 433
Table of Contents provided by Ingram. All Rights Reserved.

Please wait while the item is added to your bag...

FREE SHIPPING

Amazon no longer offers textbook rentals. We do!

Data Modeling Fundamentals A Practical Guide for IT Professionals

Summary

Author Biography

Table of Contents

Supplemental Materials