Includes bibliographical references (p. 423-424) and index.

Preface. Acknowledgments. PART I: INTRODUCTION TO DATA MODELING. 1. Data Modeling: An Overview. Chapter Objectives. Data Model Defined. What is a Data Model? Why Data Modeling? Who Performs Data Modeling? Information Levels. Classification of Information Levels. Data Models at Information Levels. Conceptual Data Modeling. Data Model Components. Data Modeling Steps. Data Model Quality. Significance of Data Model Quality. Data Model Characteristics. Ensuring Data Model Quality. Data System Development. Data System Development Life Cycle (DDLC). Roles and Responsibilities. Modeling the Information Requirements. Applying Agile Modeling Principles. Data Modeling Approaches and Trends. Data Modeling Approaches. Modeling for Data Warehouse. Other Modeling Trends. Chapter Summary. Review Questions. 2. Methods, Techniques, and Symbols. Chapter Objectives. Data Modeling Approaches. Semantic Modeling. Relational Modeling. Entity-Relationship Modeling. Binary Modeling. Methods and Techniques. Peter Chen (E-R) Modeling. Information Engineering. IDEF1X. Richard Barker's. ORM (Object Role Modeling). XML (eXtensible Markup Language). Summary and Comments. Unified Modeling Language (UML). Data Modeling Using UML. UML in the Development Process. Chapter Summary. Review Questions. PART II. DATA MODELING FUNDAMENTALS. 3. Anatomy of a Data Model. Chapter Objectives. Data Model Composition. Models at Different Levels. Conceptual Model: Review Procedure. Conceptual Model: Identifying Components. Case Study. Description. E-R Model. UML Model. Creation of Models. User Views. View Integration. Entity Types. Specialization/Generalization. Relationships. Attributes. Identifiers. Review of the Model Diagram. Logical Model: Overview. Model Components. Transformation Steps. Relatio

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.