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9781576108727

Data Modeling Essentials : A Comprehensive Guide to Data Analysis, Design, and Innovation

by
  • ISBN13:

    9781576108727

  • ISBN10:

    1576108724

  • Edition: 2nd
  • Format: Paperback
  • Copyright: 2000-12-01
  • Publisher: Coriolis Group
  • Purchase Benefits
List Price: $49.99

Author Biography

Graeme C. Simsion is Principal of Simsion Bowles and Associates, a division of Tier Technologies, Inc., in Melbourne, Australia, and is a regular speaker and publisher on the subjects of data modeling and data management. Graham C. Witt is a Senior Consultant with Simsion Bowles and Associates, specializing in data modeling, database design, and data management. He is also an experienced teacher of data modeling and publishes regularly.

Table of Contents

Part I: Overview of Data Modeling 1(194)
What is Data Modeling?
3(32)
Introduction
3(1)
A Data-Centered Perspective
3(1)
A Simple Example
4(2)
Design, Choice, and Creativity
6(2)
Why Is the Data Model so Important?
8(3)
Leverage
8(2)
Conciseness
10(1)
Data Quality
10(1)
Evaluation Criteria
11(4)
Completeness
11(1)
Nonredundancy
11(1)
Enforcement of Business Rules
11(1)
Data Reusability
12(1)
Stability and Flexibility
12(2)
Simplicity and Elegance
14(1)
Communication Effectiveness
15(1)
Conflicting Objectives
15(1)
Performance and Implementation Issues
16(6)
Logical and Physical Database Designs
16(4)
Modifying the Data Model to Improve Performance
20(1)
Roles of Data Modeler and Physical Database Designer
20(1)
When Performance Is Paramount
21(1)
Where Does the Data Model Fit In?
22(4)
Function-Driven Approaches
22(1)
Data-Driven Approaches
23(1)
Data-Driven versus Function-Driven
24(1)
Object-Oriented Approaches
24(1)
Prototyping Approaches
25(1)
Who Should Be Involved in Data Modeling?
26(1)
Costs and Benefits of Data Modeling
27(1)
Is Data Modeling Still Relevant?
28(2)
Terminology - ``Data Model''
30(1)
Alternative Approaches to Data Modeling
31(1)
Where to from Here?
31(2)
Summary
33(2)
Basic Normalization
35(32)
An Informal Example of Normalization
35(4)
Relational Notation
39(1)
A More Complex Example
40(2)
Determining Columns
42(3)
One Fact per Column
42(1)
Hidden Data
43(1)
Derivable Data
43(1)
Determining the Key
44(1)
Repeating Groups and First Normal Form
45(5)
Limit on Maximum Number of Occurrences
45(1)
Data Reusability and Program Complexity
46(1)
Recognizing Repeating Groups
46(1)
Removing Repeating Groups
47(1)
Determining the Key of the New Table
48(1)
First Normal Form
49(1)
Second and Third Normal Forms
50(7)
Problems with Tables in First Normal Form
50(1)
Eliminating Redundancy
50(1)
Determinants
51(3)
Third Normal Form
54(3)
Definitions and a Few Refinements
57(5)
Determinants and Functional Dependency
57(1)
Primary Keys
57(1)
Candidate Keys
57(1)
A Formal Definition of Third Normal Form
58(1)
Foreign Keys
59(1)
Referential Integrity
60(1)
Denormalization and Unnormalization
60(2)
Choice, Creativity, and Normalization
62(4)
Terminology
66(1)
Summary
66(1)
The Entity-Relationship Approach
67(44)
Introduction
67(1)
A Diagrammatic Representation
68(8)
The Basic Symbols
69(1)
Diagrammatic Representation of Foreign Keys
70(1)
Interpreting the Diagram
70(1)
Optionality
71(1)
Verifying the Model
72(2)
Redundant Lines
74(2)
The Top-Down Approach: Entity-Relationship Modeling
76(3)
Developing the Diagram Top Down
77(1)
Terminology
78(1)
Entities
79(7)
Entity Diagramming Convention
80(1)
Entity Naming
81(1)
Entity Definitions
82(4)
Relationships
86(18)
Relationships Diagramming Conventions
86(5)
Many-to-Many Relationships
91(6)
One-to-One Relationships
97(1)
Self-Referencing Relationships
98(3)
Relationships of Higher Degree
101(1)
Relationship Names
102(2)
Attributes
104(1)
Using UML Object Class Diagrams
105(1)
Creativity and E-R Modeling
106(3)
Summary
109(2)
Subtypes and Supertypes
111(38)
Introduction
111(1)
Different Levels of Generalization
111(3)
Rules versus Stability
114(2)
Representation of Subtypes and Supertypes
116(2)
Subtypes and Supertypes as Entities
118(1)
Diagramming Conventions
119(1)
Definitions
120(1)
Attributes and Primary Keys of Supertypes and Subtypes
121(1)
Conversion to a Relational Model
122(7)
Implementation at a Single Level of Generalization
122(2)
Implementation at Multiple Levels of Generalization
124(1)
Construction of Views
125(1)
What Happens to Relationships?
126(3)
Nonoverlapping and Exhaustive
129(2)
Overlapping Subtypes and Roles
131(2)
Ignoring Real World Overlaps
131(1)
Modeling Only the Supertype
132(1)
Modeling the Roles as Participation in Relationships
132(1)
Using Role Entities and One-to-One Relationships
133(4)
Multiple Partitions
134(3)
Hierarchy of Subtypes
137(1)
Benefits of Using Subtypes and Supertypes
137(6)
Creativity
137(1)
Presentation - Level of Detail
138(2)
Communication
140(1)
Classifying Common Patterns
141(1)
Divide and Conquer
142(1)
When Do We Stop Supertyping and Subtyping?
143(4)
Differences in Keys
143(1)
Different Attribute Groups
144(1)
Different Relationships
144(1)
Different Processes
145(1)
Migration from One Subtype to Another
145(1)
Communication
145(1)
Capturing Meaning and Rules
146(1)
Summary
147(1)
Theoretical Background
147(1)
Summary
148(1)
Putting It All Together
149(26)
Designing Real Models
149(2)
Getting Started - Identifying Entities
151(1)
Patterns and Generic Models
152(9)
Using Patterns
152(2)
Using a Generic Model
154(1)
Adapting Generic Models from Other Applications
155(5)
Developing a Generic Model
160(1)
When There Isn't a Generic Model
161(1)
Bottom-Up Modeling
161(5)
Top-Down Modeling
166(1)
When the Problem is Too Complex
167(2)
Developing Entity Definitions
169(1)
Handling Exceptions
170(1)
The Right Attitude
171(3)
Being Aware
171(1)
Being Creative
172(1)
Analyzing or Designing
172(1)
Being Brave
173(1)
Having a Plan
173(1)
Being Understanding and Understood
174(1)
Summary
174(1)
Organizing the Data Modeling Task
175(20)
Data Modeling in the Real World
175(1)
Roles and Responsibilities
176(2)
Sequence
178(2)
Inputs to the Modeling Task
180(9)
Interviews and Workshops
180(4)
Existing Systems and Reverse Engineering
184(2)
Function Models
186(3)
Testing the Model
189(3)
Comparison with the Function Model
189(1)
Review with the Users
190(1)
Sample Data
191(1)
Prototypes
191(1)
Packaging It Up
192(1)
Maintaining the Model
193(1)
Summary
194(1)
Part II: Advanced Data Modeling 195(242)
More About Relationships and Foreign Keys
197(40)
Introduction
197(1)
Translating Relationships into Foreign Keys
198(9)
The Basic Rule
198(1)
Overlapping Foreign Keys
199(2)
Split Foreign Keys
201(2)
Derivable Foreign Keys
203(4)
Optional Relationships
207(1)
Transferability
208(4)
The Concept of Transferability
209(1)
The Importance of Transferability
210(1)
Documenting Transferability
210(2)
One-to-One Relationships
212(7)
One-to-One Relationships and Creativity
212(2)
When to Use One-to-One Relationships
214(4)
Implementing One-to-One Relationships
218(1)
Self-Referencing Relationships
219(8)
Hierarchies (One-to-Many Relationships)
219(3)
Networks (Many-to-Many Relationships)
222(3)
Chains (One-to-One Relationships)
225(2)
Relationships Involving Three or More Entities
227(1)
Generalization of Relationships
227(6)
Generalizing Multiple One-to-Many Relationships to a Single Many-to-Many Relationship
231(1)
Generalizing Multiple One-to-Many Relationships to a Single One-to-Many Relationship
232(1)
Myths and Folklore
233(2)
Entities without Relationships
233(1)
Allowed Combinations of Cardinality and Optionality
234(1)
Summary
235(2)
Advanced Normalization
237(30)
Introduction
237(1)
Introduction to the Higher Normal Forms
238(1)
Common Misconceptions
238(1)
Boyce-Codd Normal Form
239(6)
Example of Structure in 3NF but Not in BCNF
239(3)
Definition of BCNF
242(1)
Enforcement of Rules Versus BCNF
243(1)
A Note on Domain Key Normal Form
244(1)
Fourth Normal Form (4NF) and Fifth Normal Form (5NF)
245(11)
Data in BCNF but Not in 4NF
245(3)
Fifth Normal Form (5NF)
248(4)
Recognizing 4NF and 5NF Situations
252(3)
Checking for 4NF and 5NF with the Business Specialist
255(1)
Further Reading on Higher Normal Forms
255(1)
Beyond 5NF - Splitting Tables Based on Candidate Keys
256(1)
Other Normalization Issues
257(8)
Normalization and Redundancy
257(2)
Look-Up Tables Produced by Normalization
259(4)
Selecting the Primary Key after Removing Repeating Groups
263(1)
Sequence of Normalization and Cross-Table Anomalies
264(1)
Advanced Normalization in Perspective
265(1)
Summary
266(1)
Primary Keys and Identity
267(24)
Basic Requirements and Trade-Offs
267(2)
Applicability
269(2)
Special Cases
269(1)
Data Unavailable at Time of Entry
270(1)
Broadening of Scope
270(1)
Uniqueness
271(1)
Minimality
272(1)
Stability
273(2)
Structured Keys and Weak Entities
275(7)
Problems with Structured Keys
275(1)
Using Structured Keys to Control Transferability
276(1)
Weak Entities
277(2)
Programming and Structured Keys
279(1)
Performance Issues with Structured Keys
280(2)
Surrogate Keys
282(4)
When to Use Surrogate Keys
282(1)
Should Surrogate Keys Be Visible?
283(1)
Surrogate Keys and Subtypes
284(1)
Singularity
285(1)
Null and Partially-Null Keys
286(2)
Multiple Candidate Keys
288(1)
Choosing a Primary Key
288(1)
Normalization Issues
288(1)
Summary
289(2)
Attributes
291(36)
Introduction
291(1)
Attribute Definition
292(1)
Attribute Disaggregation - One Fact per Attribute
293(6)
Simple Aggregation
295(1)
Complex Codes
296(1)
Meaningful Ranges
297(1)
Inappropriate Generalization
298(1)
Is a Foreign Key an Attribute?
299(1)
Types of Attributes
300(10)
The Attribute Taxonomy in Detail
301(5)
Attribute Datatype and Length Requirements
306(3)
Conversion between External and Internal Representations
309(1)
Attribute Names
310(4)
Objectives of Standardizing Attribute Names
310(1)
Some Guidelines for Attribute Naming
311(3)
Attribute Generalization
314(11)
Options and Trade-Offs
314(1)
Attribute Generalization Resulting From Entity Generalization
315(2)
Attribute Generalization within Entities
317(6)
Limits to Attribute Generalization
323(2)
Summary
325(2)
Time-Dependent Data
327(24)
The Problem
327(1)
Audit Trails and Snapshots
328(10)
The Basic Audit Trail Approach
329(5)
Handling Nonnumeric Data
334(1)
The Basic Snapshot Approach
335(3)
Sequences and Versions
338(1)
Handling Deletions
338(1)
Archiving
339(1)
Modeling Time-Dependent Relationships
340(7)
One-to-Many Relationships
340(5)
Many-to-Many Relationships
345(2)
Self-Referencing Relationships
347(1)
Date Entities
347(2)
Changes to the Data Structure
347(2)
Putting it into Practice
349(1)
Summary
350(1)
Modeling Business Rules
351(38)
Introduction
351(2)
Types of Business Rules
353(3)
Data Rules
353(2)
Process Rules
355(1)
What Rules are Relevant to the Data Modeler?
355(1)
Discovery and Verification of Business Rules
356(2)
Cardinality Rules
356(1)
Data Validation Rules
357(1)
Data Derivation Rules
357(1)
Documentation of Business Rules
358(4)
Documentation in an Entity-Relationship Diagram
358(1)
Documentation in a Relational Model
359(1)
Documenting Other Rules
360(2)
Use of Subtypes to Document Rules
362(1)
Implementation of Business Rules
362(26)
Implementing Particular Rules
366(5)
Implementation Options: a Detailed Example
371(4)
Implementing Mandatory Relationships
375(2)
Referential Integrity
377(1)
Restricting an Attribute to a Discrete Set of Values
378(4)
Rules Involving Multiple Attributes
382(1)
Recording Data that Supports Rules
383(3)
Recording Information about Breaking the Rules
386(1)
Enforcement of Rules through Primary Key Selection
387(1)
Summary
388(1)
Modeling for Data Warehouses and Data Marts
389(26)
Introduction
389(3)
Characteristics of Data Warehouses and Data Marts
392(3)
Data Integration - Working with Existing Databases
392(1)
Loads Rather than Updates
393(1)
Less Predictable Database ``Hits''
393(1)
Complex Queries - Simple Interface
394(1)
Summarization
394(1)
History
394(1)
Quality Criteria for Warehouse and Mart Models
395(3)
Completeness
395(1)
Nonredundancy
396(1)
Enforcement of Business Rules
396(1)
Data Reusability
397(1)
Stability and Flexibility
397(1)
Simplicity and Elegance
397(1)
Communication Effectiveness
398(1)
Performance
398(1)
The Basic Design Principle
398(1)
Modeling for the Data Warehouse
399(4)
An Initial Model
400(1)
Understanding Existing Data
400(1)
Determining Requirements
401(1)
Determining Sources and Dealing with Differences
401(1)
Shaping Data for Data Marts
402(1)
Modeling for the Data Mart
403(11)
The Basic Challenge
403(1)
Multidimensional Databases, Stars and Snowflakes
404(7)
Modeling Time-Dependent Data
411(3)
Summary
414(1)
Corporate Data Modeling
415(22)
Introduction
415(1)
Data Management
416(3)
Problems of Data Mismanagement
416(2)
Managing Data as a Shared Resource
418(1)
Classification of Existing Data
419(1)
A Target for Planning
420(2)
A Context for Specifying New Databases
422(2)
Determining Scope and Interfaces
422(1)
Incorporating the Corporate Data Model in the Development Lifecycle
422(2)
Guidance for Database Design
424(1)
Input to Business Planning
425(1)
Specification of a Corporate Database
426(2)
Characteristics of Corporate Data Models
428(1)
Developing a Corporate Data Model
429(4)
The Development Cycle
429(1)
Partitioning the Task
430(1)
Inputs to the Task
431(1)
Expertise Requirements
432(1)
External Standards
433(1)
Choice, Creativity, and Corporate Data Models
433(2)
Summary
435(2)
Appendices 437(14)
Appendix A: Check List for Data Model Review
439(6)
The Context of the Model
439(1)
The Model as a Whole
440(1)
Entities
441(1)
Relationships
442(1)
Attributes
443(1)
Keys and Normalization
443(1)
Business Rules
444(1)
Appendix B: An Approach to Presenting Large Data Models
445(6)
Step 1: Partition Data Model into Subject Areas
445(1)
Step 2: Produce Context Data Model
446(1)
Step 3: Produce Subject Area Data Models
447(4)
Index 451

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