Data Modeling Essentials

Data Modeling Essentials

3rd Edition - November 4, 2004

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  • Authors: Graeme Simsion, Graham Witt
  • eBook ISBN: 9780080488677

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Data Modeling Essentials, Third Edition, covers the basics of data modeling while focusing on developing a facility in techniques, rather than a simple familiarization with "the rules". In order to enable students to apply the basics of data modeling to real models, the book addresses the realities of developing systems in real-world situations by assessing the merits of a variety of possible solutions as well as using language and diagramming methods that represent industry practice. This revised edition has been given significantly expanded coverage and reorganized for greater reader comprehension even as it retains its distinctive hallmarks of readability and usefulness. Beginning with the basics, the book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. It includes an entirely new section discussing the development of logical and physical modeling, along with new material describing a powerful technique for model verification. It also provides an excellent resource for additional lectures and exercises. This text is the ideal reference for data modelers, data architects, database designers, DBAs, and systems analysts, as well as undergraduate and graduate-level students looking for a real-world perspective.

Key Features

  • Thorough coverage of the fundamentals and relevant theory
  • Recognition and support for the creative side of the process
  • Expanded coverage of applied data modeling includes new chapters on logical and physical database design
  • New material describing a powerful technique for model verification
  • Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict


Data modelers, data architects, database designers, DBAs, systems analysts; undergraduate and graduate-level students

Table of Contents

  • Chapter 1 - What Is Data Modeling?
    1.1 Introduction
    1.2 A Data-Centered Perspective
    1.3 A Simple Example
    1.4 Design, Choice, and Creativity
    1.5 Why Is the Data Model Important?
    1.6 What Makes a Good Data Model?
    1.7 Performance
    1.8 Database Design Stages and Deliverables
    1.9 Where Do Data Models Fit In?
    1.10 Who Should Be Involved in Data Modeling?
    1.11 Is Data Modeling Still Relevant?
    1.12 Alternative Approaches to Data Modeling
    1.13 Terminology
    1.14 Where to from Here?—An Overview of Part 1
    1.15 Summary

    Chapter 2 - Basics of Sound Structure
    2.1 Introduction
    2.2 An Informal Example of Normalization
    2.3 Relational Notation
    2.4 A More Complex Example
    2.5 Determining Columns
    2.6 Repeating Groups and First Normal Form
    2.7 Second and Third Normal Forms
    2.8 Definitions and a Few Refinements
    2.9 Choice, Creativity, and Normalization
    2.10 Terminology 60
    2.11 Summary 61

    Chapter 3 - The Entity-Relationship Approach
    3.1 Introduction
    3.2 A Diagrammatic Representation
    3.3 The Top-Down Approach: Entity-Relationship Modeling
    3.4 Entity Classes
    3.5 Relationships
    3.6 Attributes
    3.7 Myths and Folklore
    3.8 Creativity and E-R Modeling
    3.9 Summary

    Chapter 4 - Subtypes and Supertypes
    4.1 Introduction
    4.2 Different Levels of Generalization
    4.3 Rules Versus Stability
    4.4 Using Subtypes and Supertypes
    4.5 Subtypes and Supertypes as Entity Classes
    4.6 Diagramming Conventions
    4.7 Definitions
    4.8 Attributes of Supertypes and Subtypes
    4.9 Non-Overlapping and Exhaustive
    4.10 Overlapping Subtypes and Roles
    4.11 Hierarchy of Subtypes
    4.12 Benefits of Using Subtypes and Supertypes
    4.13 When Do We Stop Supertyping and Subtyping?
    4.14 Generalization of Relationships
    4.15 Theoretical Background
    4.16 Summary

    Chapter 5 - Attributes and Columns
    5.1 Introduction
    5.2 Attribute Definition
    5.3 Attribute Disaggregation: One Fact Per Attribute
    5.4 Types of Attributes
    5.5 Attribute Names
    5.6 Attribute Generalization
    5.7 Summary

    Chapter 6 - Primary Keys and Identity
    6.1 Basic Requirements and Trade-Offs
    6.2 Basic Technical Criteria
    6.3 Surrogate Keys
    6.4 Structured Keys
    6.5 Multiple Candidate Keys
    6.6 Guidelines for Choosing Keys
    6.7 Partially-Null Keys
    6.8 Summary

    Chapter 7 - Extensions & Alternatives
    7.1 Introduction
    7.2 Extensions to the Basic E-R Approach
    7.3 The Chen E-R Approach
    7.4 Using UML Object Class Diagrams
    7.5 Summary

    Chapter 8 - Organizing the Data Modeling Task
    8.1 Data Modeling in the Real World
    8.2 Key Issues in Project Organization
    8.3 Roles and Responsibilities
    8.4 Partitioning Large Projects
    8.5 Maintaining the Model
    8.6 Packaging It Up
    8.7 Summary

    Chapter 9 - The Business Requirements
    9.1 Purpose of the Requirements Phase
    9.2 The Business Case
    9.3 Interviews and Workshops
    9.4 Riding the Trucks
    9.5 Existing Systems and Reverse Engineering
    9.6 Process Models
    9.7 Object Class Hierarchies
    9.8 Summary

    Chapter 10 - Conceptual Data Modeling
    10.1 Designing Real Models
    10.2 Learning from Designers in Other Disciplines
    10.3 Starting the Modeling
    10.4 Patterns and Generic Models
    10.5 Bottom-Up Modeling
    10.6 Top-Down Modeling
    10.7 When the Problem is Too Complex
    10.8 Hierarchies, Networks, and Chains
    10.9 One-to-One Relationships
    10.10 Developing Entity Class Definitions
    10.11 Handling Exceptions
    10.12 The Right Attitude
    10.13 Evaluating the Model
    10.14 Direct Review of Data Model Diagrams
    10.15 Comparison with the Process Model
    10.16 Testing the Model with Sample Data
    10.17 Prototypes
    10.18 The Assertions Approach
    10.19 Summary

    Chapter 11 - Logical Database Design
    11.1 Introduction
    11.2 Overview of the Transformations Required
    11.3 Table Specification
    11.4 Basic Column Definition
    11.5 Primary Key Specification
    11.6 Foreign Key Specification
    11.7 Table and Column Names
    11.8 Logical Data Model Notations
    11.9 Summary

    Chapter 12 - Physical Database Design
    12.1 Introduction
    12.2 Inputs to Database Design
    12.3 Options Available to the Database Designer
    12.4 Design Decisions Which Do Not Affect Program Logic
    12.5 Crafting Queries to Run Faster
    12.6 Logical Schema Decisions
    12.8 Summary

    Chapter 13 - Advanced Normalization
    13.1 Introduction
    13.2 Introduction to the Higher Normal Forms
    13.3 Boyce-Codd Normal Form
    13.4 Fourth Normal Form (4NF) and Fifth Normal Form (5NF)
    13.5 Beyond 5NF: Splitting Tables Based on Candidate Keys
    13.6 Other Normalization Issues
    13.7 Advanced Normalization in Perspective
    13.8 Summary

    Chapter 14 - Modeling Business Rules
    14.1 Introduction
    14.2 Types of Business Rules
    14.3 Discovery and Verification of Business Rules
    14.4 Documentation of Business Rules
    14.5 Implementing Business Rules
    14.6 Rules on Recursive Relationships
    14.7 Summary

    Chapter 15 - Time-Dependent Data
    15.1 The Problem
    15.2 When Do We Add the Time Dimension?
    15.3 Audit Trails and Snapshots
    15.4 Sequences and Versions
    15.5 Handling Deletions
    15.6 Archiving
    15.7 Modeling Time-Dependent Relationships
    15.8 Date Tables
    15.9 Temporal Business Rules
    15.10 Changes to the Data Structure
    15.11 Putting it into Practice
    15.12 Summary

    Chapter 16 - Modeling for Data Warehouses and Data Marts
    16.1 Introduction
    16.2 Characteristics of Data Warehouses and Data Marts
    16.3 Quality Criteria for Warehouse and Mart Models
    16.4 The Basic Design Principle
    16.5 Modeling for the Data Warehouse
    16.6 Modeling for the Data Mart
    16.7 Summary

    Chapter 17 - Enterprise Data Models and Data Management
    17.1 Introduction
    17.2 Data Management
    17.3 Classification of Existing Data
    17.4 A Target for Planning
    17.5 A Context for Specifying New Databases
    17.6 Guidance for Database Design
    17.7 Input to Business Planning
    17.8 Specification of an Enterprise Database
    17.9 Characteristics of Enterprise Data Models
    17.10 Developing an Enterprise Data Model
    17.11 Choice, Creativity, and Enterprise Data Models
    17.12 Summary

Product details

  • No. of pages: 560
  • Language: English
  • Copyright: © Morgan Kaufmann 2004
  • Published: November 4, 2004
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780080488677

About the Authors

Graeme Simsion

Graeme C. Simsion has over 25 years experience in information systems as a DBA, data modeling consultant, business systems designer, manager, and researcher. He is a regular presenter at industry and academic forums, and is currently a Senior Fellow with the Department of Information Systems at the University of Melbourne.

Affiliations and Expertise

Senior Fellow, University of Melbourne, Australia

Graham Witt

Graham C. Witt is an independent consultant with over 30 years of experience in assisting enterprises to acquire relevant and effective IT solutions. His clients include major banks and other financial institutions; businesses in the insurance, utilities, transport and telecommunications sectors; and a wide variety of government agencies. A former guest lecturer on Database Systems at University of Melbourne, he is a frequent presenter at international data management conferences.

Affiliations and Expertise

Independent Consultant, Sydney, Australia

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  • Eduard O. Wed Jan 24 2018

    Data modeling Essentials

    Fantastic book. Very easy to understand and gives very good tips on how to approach a problem.