From the website:
Anyone charged with developing a data model knows that there is a wide variety of potential problems likely to arise before achieving a high quality data model. With dozens of attributes and millions of rows, data modelers are in always danger of inconsistency and inaccuracy. The development of the data model itself could result in difficulties presenting accurate data. The need to improve data models begins in getting it right in the first place.
Developing High Quality Data Models uses real-world examples to show you how to identify a number of data modeling principles and analysis techniques that will enable you to develop data models that consistently meet business requirements. A variety of generic data model patterns that exemplify the principles and techniques discussed build upon one another to give a powerful and integrated generic data model with wide applicability across many disciplines. The principles and techniques outlined in this book are applicable in government and industry, including but not limited to energy exploration, healthcare, telecommunications, transportation, military defense, transportation and so on.
Table of Contents:
Chapter 1- Introduction
Chapter 2- Entity Relationship Model Basics
Chapter 3- Some types and uses of data models
Chapter 4- Data models and enterprise architecture
Chapter 5- Some observations on data models and data modeling
Chapter 6- Some General Principles for Conceptual, Integration and Enterprise Data Models
Chapter 7- Applying the principles for attributes
Chapter 8- General principles for relationships
Chapter 9- General principles for entity types
Chapter 10- Motivation and overview for an ontological framework
Chapter 12- Classes
Chapter 13- Intentionally constructed objects
Chapter 14- Systems and system components
Chapter 15- Requirements specifications
Chapter 16- Concluding Remarks
Chapter 17- The HQDM Framework Schema
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