Data Modeling Fundamentals: A Practical Guide for IT Professionals

A ONE–STOP, DEFINITIVE GUIDE TO DATA MODELING FOR IT PROFESSIONALS
Data Modeling Fundamentals is a comprehensive guide to the foundation and principles behind data modeling and its essential role in successful database design. The only book specifically addressing the essentials of data modeling for IT professionals, it includes:
An overview of data modeling concepts, techniques, and symbols
Anatomy of a comprehensive data model describing the various components
Extensive details of entities, attributes, identifiers, and relationships
Transition from data modeling to database design and implementation, datanormalization, and modeling for decision–support systems
Quality data modeling and agile data modeling in practice
Trends in data modeling methodologies and techniques
Chapter summaries with review questions and exercises at the conclusion of each chapter help readers gauge their understanding of the material presented. Complete with real–world examples, references, and an abundance of practical tips and suggestions, Data Modeling Fundamentals is an indispensable resource for both novice and experienced data modelers.

Spis treści:
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 Mod eling 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.
Relational Model.
Physical Model: Overview.
Model Components.
Transformation Steps.
Chapter Summary.
Review Questions.
4. Objects or Entities in Detail.
Chapter Objectives.
Entity Types or Object Sets.
Comprehensive Definition.
Identifying Entity Types.
Homonyms and Synonyms.
Category of Entity Types.
Exploring Dependencies.
Dependent or Weak Entity Types.
Classifying Dependencies.
Representation in the Model.
Generalization and Specialization.
Why Generalize or Specialize?
Super–types and Sub–types.
Generalization Hierarchy.
Inheritance of Attributes.
Inheritance of Relat ionships.
Constraints.
Rules Summarized.
Special Cases and Exceptions.
Recursive Structures.
Conceptual and Physical.
Assembly Structures.
Entity Type Vs Attribute.
Entity Type Vs Relationship.
Modeling Time Dimension.
Categorization.
Entity Validation Checklist.
Completeness.
Correctness.
Chapter Summary.
Review Questions.
5. Attributes and Identifiers in Detail.
Chapter Objectives.
Attributes.
Properties or Characteristics.
Attributes as Data.
Attribute Values.
Names and Descriptions.
Attribute Domains.
Definition of a Domain.
Domain Information.
Attribute Values and Domains.
Split Domains.
Misrepresented Domains.
Resolution of Mixed Domains.
Constraints for Attributes.
Value Set.
Range.
Type.
Null Values.
Types of Attributes.
Single–Valued and Multi–Valued Attributes.
Simple and Composite Attributes.
Attributes with Stored and Derived Values .
Optional Attributes.
Identifiers or Keys.
Need for Identifiers.
Definitions of Keys.
Guidelines for Identifiers.
Key in Generalization Hierarchy.
Attribute Validation Checklist.
Completeness.
Correctness.
Chapter Summary.
Review Questions.
6. Relationships in Detail.
Chapter Objectives.
Relationships.
Associations.
Relationship?Two–sided.
Relationship Sets.
Double Relationships.
Relationship Attributes.
Degree of Relationships.
Unary Relationship.
Binary Relationship.
Ternary Relationship.
Quaternary Relationship.
Structural Constraints.
Cardinality Constraint.
Participation Constraint.
Dependencies.
Entity Existence.
Relationship Types.
Identifying Relationship .
Non–identifying Relationship.
Maximum and Minimum Cardinalities.
Mandatory Conditions – Both Ends.
Optional Condition – One End.
Optional Condition – Other End.
Optional Conditions – Both Ends.
Special Cases.
Gerund.
Aggregation.
Access Pathways.
Design Issues.
Relationship Or Entity Type?
Ternary Relationship Or Aggregation?
Binary Or N–ary Relationship?
One–to–One Relationships.
One–to–Many Relationships.
Circular Structures.
Redundant Relationships.
Multiple Relationships.
Relationship Validation Checklist.
Completeness.
Correctness.
Chapter Summary.
Review Questions.
PART III. DATA MODEL IMPLEMENTATION.
7. Data Modeling to Database Design.
Chapter Objectives.
Relational Model: Fundamentals.
Basic Concepts.
Structure and Components.
Data Integrity Constraints.
Transition to Database Design.
Design Approaches.
Conceptual to Relational Model.
Traditional Method.
Evaluation of Design Methods.
Model Transformation Method.
The Approach.
Mapping of Components.
Entity Types to Relations.
Attributes to Columns.
Identifiers to Keys.
Transformation of Relationships.
Transformation Summary .
Chapter Summary.
Review Questions.
8. Data Normalization.
Chapter Objectives.
Informal Design.
Forming Relations from Requirements.
Potential Problems.
Update Anomaly.
Deletion Anomaly.
Addition Anomaly.
Normalization Methodology.
Strengths of the Method.
Application of the Method.
Normalization Steps.
Fundamental Normal Forms.
First Normal Form.
Second Normal Form.
Third Normal Form.
Boyce–Codd Normal Form.
Higher Normal Forms.
Fourth Normal Form.
Fifth Normal Form.
Domain–Key Normal Form.
Normalization Summary.
Review of the Steps.
Normalization as Verification.
Chapter Summary.
Review Questions.
9. Modeling for Decision–Support Systems.
Chapter Objectives.
Decision–Support Systems.
Need for Strategic Information.
History of Decision–Support Systems.
Operat