Data Modeling Essentials: Analysis, Design and Innovation / Edition 2

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Data Modeling Essentials is a comprehensive guide to data modeling for commercial information systems. Written by a leading practitioner, it is intended as a tutorial and reference for systems analysts, business analysts, specialist data modelers, database designers, and students requiring a practical understanding of the subject. This book presents data modeling as a design activity which offers opportunities for creativity and innovation. Many practical examples are used to illustrate the choices available to modelers and criteria for selecting the best model. Subjects covered in detail include organizing the modeling task; managing compromises; subtypes and supertypes; designing for flexibility; basic and advanced normalization; developing and using generic models; modeling the time dimension; and corporate data modeling. Conventions and language used in the book reflect those widely used in industry and supported by popular CASE products.
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Product Details

  • ISBN-13: 9781850328773
  • Publisher: Cengage Learning
  • Publication date: 6/1/1996
  • Series: Itcp Computer Science Ser.
  • Format: Mass Market Paperback
  • Edition number: 2
  • Pages: 336
  • Product dimensions: 7.41 (w) x 9.23 (h) x 0.96 (d)

Table of Contents

Pt. I Overview of Data Modeling 1
Ch. 1 What Is Data Modeling? 3
Ch. 2 Basic Normalization 25
Ch. 3 The Entity Relationship Approach 51
Ch. 4 Subtypes and Supertypes 83
Ch. 5 Putting It All Together 113
Pt. II Advanced Data Modeling 145
Ch. 6 More About Relationships and Foreign Keys 147
Ch. 7 Advanced Normalization 177
Ch. 8 Primary Keys and Identity 201
Ch. 9 Attributes 221
Ch. 10 The Time Dimension 243
Ch. 11 Modeling Business Rules 261
Ch. 12 Corporate Data Modeling 279
Appendices 295
Appendix A. Check List for Data Model Review 297
Index 303
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  • Anonymous

    Posted August 15, 2005


    Once you have a basic understanding of your application development tool, it will be a lot easier to learn the principles of data modeling. Authors Graeme Simsion and Graham Wittdone have done an outstanding job in this book of helping IT professionals to acquire competency in data modeling. Simsion and Wittdone begin this book by covering the basics of data modeling. Next, the authors look at some fundamental techniques for organizing data. In addition, the authors present a top-down approach to data modeling, supported by a widely used diagramming convention. They also look at a particular and very important type of choice in data modeling. Then, they turn to the nuts and bolts of data: attributes and columns. The authors then look in detail at the technical criteria governing primary key selection. Next, they look at some of the more common alternatives and extensions, focusing on conceptual modeling. Then, they look at the critical data modeling issues in project planning and management, with the aim of giving you the tools to examine critically any proposed approach from a data modeling perspective. The authors continue by looking at a variety of techniques for gaining a holistic understanding of the relevant business area and the role of the proposed information system. Next, they cover the development and use of a repertoire of standard solutions that are a large part of practical data modeling. In addition, they then look at the most common situation and describe the transformations and design decisions that are needed to apply to the conceptual model to produce a logical model suitable for direct implementation as a relational database. The authors then review the inputs that the physical database designer requires in addition to the Logical Data Model as well as, looking at a number of options available for achieving performance goals. Next, they look at three further stages of normalization: Boyce-Codd normal form (BCNF), fourth normal form (4NF), and fifth normal for (5NF). They then continue to look in a broad fashion at the business rules and then focus on the types of rules that are of particular concern to the data modeler. In addition, the authors look at some basic principles and structures for handling time-related data. Next, they look at how the requirements for data marts and data warehouses differ from those for operational databases. Finally, they look briefly at data management in general, and then discuss the uses of enterprise data models. With the preceding in mind, the authors have done an excellent job of showing how to develop enterprise data modeling. At the same time, the authors caution that 'while enterprise data models can be powerful vehicles for promulgating new ideas, they may also stifle original thinking by requiring conformity.'

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