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Chapter 1 Introduction
1.1 Data and Database Management
1.2 The Database Life Cycle
1.2 Conceptual Data Modeling
Chapter 2 The Entity-Relationship Model
2.1 Fundamental ER Constructs
2.1.1 Basic Objects: Entities, Relationships, Attributes
2.1.2 Degree of a Relationship
2.1.3 Connectivity of a Relationship
2.1.4 Attributes of a Relationship
2.1.5 Existence of an Entity in a Relationship
2.1.6 Alternative Conceptual Data Modeling Notations
2.2 Advanced ER Constructs
2.2.1 Generalization: Supertypes and Subtypes
2.2.3 Ternary Relationships
2.2.4 General n-ary Relationships
2.2.5 Exclusion constraint
2.2.6 Referential Integrity
Chapter 3 Unified Modeling Language
3.1 Class Diagrams
3.1.1 Class Diagram Notation Description
3.1.2 Class Diagrams for Software Design
3.1.3 Class Diagrams for Database Design
3.2 Activity Diagrams
3.2.1 Activity Diagram Notation Description
3.2.2 Activity Diagrams for Software Design
3.2.3 Activity Diagrams for Workflow
3.3 Rules of Thumb for UML Usage
Chapter 4 Requirements Analysis and Conceptual Data Modeling
4.2 Requirements Analysis
4.3 Conceptual Data Modeling
4.3.1 Classify Entities and Attributes
4.3.2 Identify the Generalization Hierarchies
4.3.3 Define Relationships
4.3.4 Example of Data Modeling: Company Project Database
4.4 View Integration
4.4.1 Pre-integration Analysis
4.4.2 Comparison of Schemas
4.4.3 Conformation of Schemas
4.4.4 Merging and Restructuring of Schemas
4.4.5 Example of View Integration
4.5 Entity Clustering for ER Models
4.5.1 Clustering Concepts
4.5.2 Grouping Operations
4.5.3 Clustering Technique
Chapter 5 Transforming the Conceptual Data Model to SQL
5.1 Transformation Rules and SQL Constructs
5.1.1 Binary Relationships
5.1.2 Binary Recursive Relationships
5.1.3 Ternary and n-ary Relationships
5.1.4 Generalization and Aggregation
5.1.5 Multiple Relationships
5.1.6 Weak Entities
5.2 Transformation Steps
5.2.1 Entity Transformation
5.2.2 Many-to-Many Binary Relationship Transformation
5.2.3 Ternary Relationship Transformation
5.2.4 Example of ER-to-SQL Transformation
Chapter 6 Normalization
6.1 Fundamentals of Normalization
6.1.1 First Normal Form
6.1.2 Superkeys, Candidate Keys, and Primary Keys
6.1.3 Second Normal Form
6.1.4 Third Normal Form
6.1.5 Boyce-Codd Normal Form
6.2 The Design of Normalized Tables: Simple Example
6.3 Normalization of Candidate Tables Derived from ER Diagrams
6.4 Determining the Minimum Set of 3NF Tables
6.4.1 Elimination of Extraneous Attributes
6.4.2 Search for a Nonredundant Cover
6.4.3 Partitioning of the Nonredundant Cover
6.4.4 Merge of Equivalent Keys
6.4.5 Definition of Tables
6.5 Fourth and Fifth Normal Forms
6.5.1 Multivalued Dependencies
6.5.2 Fourth Normal Form
6.5.3 Decomposing Tables to 4NF
6.5.4 Fifth Normal Form
Chapter 7 An Example of Logical Database Design
7.1 Requirements Specification
7.2 Logical Design
Chapter 8 Business Intelligence
8.1 Data Warehousing
8.1.1 Overview of Data Warehousing
8.1.2 Logical Design
8.2 On-Line Analytical Processing (OLAP)
8.2.1 The Exponential Explosion of Views
8.2.2 Overview of OLAP
8.2.3 View Size Estimation
8.2.4 Selection of Material Views
8.2.5 View Maintenance
8.2.6 Query Optimization
8.3 Data Mining
8.3.2 Text Mining
Chapter 9 CASE Tools for Logical Database Design
9.1 Introduction to Software Tools
9.2 The Key Capabilities to Watch For
9.3 The Basics
9.4 Generating a Database From a Design
9.5 Database Support
9.6 Collaborative Support
9.7 Distributed Development
9.8 Application Lifecycle Tooling Integration
9.9 Design Compliance Checking
9.11 Semi-Structured Data, XML
Appendix The Basics of SQL
A.1 SQL Names and Operators
A.2 Data Definition Language (DDL)
A.3 Data Manipulation Language (DML)
A.3.1 SQL Select Command
A.3.2 SQL Update Commands
A.3.3 Referential Integrity
A.3.4 SQL Views
Exercises for Logical Design
Solutions to Selected Exercises
Database Modeling and Design, Fourth Edition, the extensively revised edition of the classic logical database design reference, explains how you can model and design your database application in consideration of new technology or new business needs. It is an ideal text for a stand-alone data management course focused on logical database design, or a supplement to an introductory text for introductory database management.
This book features clear explanations, lots of terrific examples and an illustrative case, and practical advice, with design rules that are applicable to any SQL-based system. The common examples are based on real-life experiences and have been thoroughly class-tested. The text takes a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling - complemented with examples for both approaches. It also discusses the use of data modeling concepts in logical database design; the transformation of the conceptual model to the relational model and to SQL syntax; the fundamentals of database normalization through the fifth normal form; and the major issues in business intelligence such as data warehousing, OLAP for decision support systems, and data mining. There are examples for how to use the most popular CASE tools to handle complex data modeling problems, along with exercises that test understanding of all material, plus solutions for many exercises. Lecture notes and a solutions manual are also available.
This edition will appeal to professional data modelers and database design professionals, including database application designers, and database administrators (DBAs); new/novice data management professionals, such as those working on object oriented database design; and students in second courses in database focusing on design.
- a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling--with examples throughout the book in both approaches!
+ the details and examples of how to use data modeling concepts in logical database design, and the transformation of the conceptual model to the relational model and to SQL syntax;
+ the fundamentals of database normalization through the fifth normal form;
+ practical coverage of the major issues in business intelligence--data warehousing, OLAP for decision support systems, and data mining;
+ examples for how to use the most popular CASE tools to handle complex data modeling problems.
+ Exercises that test understanding of all material, plus solutions for many exercises.
Professional data modelers and database design professionals, including database application designers, database admininstrators (DBAs), and new/novice data management professionals, including those working on object oriented database design; students in second courses in database focusing on design.
- No. of pages:
- © Morgan Kaufmann 2005
- 6th September 2005
- Morgan Kaufmann
- eBook ISBN:
- Paperback ISBN:
"An explicit presentation on Business Intelligence is a major strength of this book. For beginners, there is an elegant presentation on SQL in the appendix and the book is supplemented by a detailed glossary. Exercises, examples and solutions constitute an important part of this book. This book is useful reading for both beginners and advanced users as the contents integrate elements that would address various audiences at different levels."
- P. Pichappan, Department of Information Science, Annamalai University, India
Toby J. Teorey is a professor in the Electrical Engineering and Computer Science Department at the University of Michigan, Ann Arbor. He received his B.S. and M.S. degrees in electrical engineering from the University of Arizona, Tucson, and a Ph.D. in computer sciences from the University of Wisconsin, Madison. He was general chair of the 1981 ACM SIGMOD Conference and program chair for the 1991 Entity-Relationship Conference. Professor Teorey’s current research focuses on database design and data warehousing, OLAP, advanced database systems, and performance of computer networks. He is a member of the ACM and the IEEE Computer Society.
University of Michigan, Ann Arbor, USA
Sam Lightstone is a Senior Technical Staff Member and Development Manager with IBM’s DB2 product development team. His work includes numerous topics in autonomic computing and relational database management systems. He is cofounder and leader of DB2’s autonomic computing R&D effort. He is Chair of the IEEE Data Engineering Workgroup on Self Managing Database Systems and a member of the IEEE Computer Society Task Force on Autonomous and Autonomic Computing. In 2003 he was elected to the Canadian Technical Excellence Council, the Canadian affiliate of the IBM Academy of Technology. He is an IBM Master Inventor with over 25 patents and patents pending; he has published widely on autonomic computing for relational database systems. He has been with IBM since 1991.
IBM, Toronto, Canada
Tom Nadeau is the founder of Aladdin Software (aladdinsoftware.com) and works in the area of data and text mining. He received his B.S. degree in computer science and M.S. and Ph.D. degrees in electrical engineering and computer science from the University of Michigan, Ann Arbor. His technical interests include data warehousing, OLAP, data mining and machine learning. He won the best paper award at the 2001 IBM CASCON Conference.
Ubiquiti Inc., Ann Arbor, MI
H.V. Jagadish is a professor in EE and CS at the University of Michigan, Ann Arbor, where he is part of the database group affiliated with the bioinformatics program and the Center for Computational Medicine and Bioinformatics. Prior to joining the Michigan faculty, he spent over a decade at AT&T Bell Laboratories as a research scientist where he became head of the Database division.
Univ of Mich, Ann Arbor (EE/CS dept)
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