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Data Modeling Essentials 3rd edition [Pehme köide]

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(Senior Fellow, University of Melbourne, Australia), (Independent Consultant, Sydney, Australia)
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Data Modeling Essentials, Third Edition provides expert tutelage for data modelers, business analysts and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management.

The third edition of this popular book retains its distinctive hallmarks of readability and usefulness, but has been given significantly expanded coverage and reorganized for greater reader comprehension. Authored by two leaders in the field, Data Modeling Essentials, Third Edition is the ideal reference for professionals and students looking for a real-world perspective.

· Thorough coverage of the fundamentals and relevant theory.
· Recognition and support for the creative side of the process.
· Expanded coverage of applied data modeling includes new chapters on logical and physical database design.
· New material describing a powerful technique for model verification.
· Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict.
· Extensive online component including course notes and other teaching aids (www.mkp.com).

Coming Soon! Please visit us again for UML versions of the data modeling diagrams.

Click here to view a book review by Steve Hoberman!

Arvustused

"The perfect balance of theory and practice, giving the reader both the foundation and the tools to deliver high-quality data models." --Karen Lopez, Principal, InfoAdvisors, Inc.

"The complete guide to data modeling for the reflective practitioner. Students like this book and so do I -- it is clear and accessible without sacrificing rigor." --Professor Graeme Shanks, School of Business Systems, Monash University, Australia

"A unique, practical and comprehensive guide, providing an invaluable resource to anyone involved in data modeling from the novice to the expert data modeler." --Len Silverston, author of The Data Model Resource Book, Volumes 1 and 2.

"Includes an extraordinary amount of good, useful, and well articulated information about the field." --David Hay, President, Essential Strategies, Inc. and author of Data Model Patterns

"Data Modeling Essentials is a fresh look at an old topic...much more accesible and refreshing in its tone and attitude. Simsion's explanations are very clear and his examples easy to follow...This book is useful for both beginners and experienced data modelers who want a new innovative approach to the important task of documenting data requirements." - The Bridge --Barbara A. Carkenord

"The book is extremely well-written. It is humorous at times, full of useful anecdotes, and follows a very logical sequence...In summary, I found Data Modeling Essentials, Third Edition, very useful for data modelers at any level of experience." --DM Review

Muu info

A popular classic, substantially revised and expanded.
Preface xxiii
Part I The Basics
1(228)
What Is Data Modeling?
3(30)
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 Important?
8(2)
Leverage
8(1)
Conciseness
9(1)
Data Quality
10(1)
Summary
10(1)
What Makes a Good Data Model?
10(5)
Completeness
10(1)
NonRedundancy
11(1)
Enforcement of Business Rules
11(1)
Data Reusability
11(1)
Stability and Flexibility
12(1)
Elegance
13(1)
Communication
14(1)
Integration
14(1)
Conflicting Objectives
15(1)
Performance
15(1)
Database Design Stages and Deliverables
16(4)
Conceptual, Logical, and Physical Data Models
16(1)
The Three-Schema Architecture and Terminology
17(3)
Where Do Data Models Fit In?
20(3)
Process-Driven Approaches
20(1)
Data-Driven Approaches
20(2)
Parallel (Blended) Approaches
22(1)
Object-Oriented Approaches
22(1)
Prototyping Approaches
23(1)
Agile Methods
23(1)
Who Should Be Involved in Data Modeling?
23(1)
Is Data Modeling Still Relevant?
24(5)
Costs and Benefits of Data Modeling
25(1)
Data Modeling and Packaged Software
26(1)
Data Integration
27(1)
Data Warehouses
27(1)
Personal Computing and User-Developed Systems
28(1)
Data Modeling and XML
28(1)
Summary
28(1)
Alternative Approaches to Data Modeling
29(1)
Terminology
30(1)
Where to from Here?---An Overview of Part I
31(1)
Summary
32(1)
Basics of Sound Structure
33(32)
Introduction
33(1)
An Informal Example of Normalization
34(2)
Relational Notation
36(1)
A More Complex Example
37(3)
Determining Columns
40(3)
One Fact per Column
40(1)
Hidden Data
41(1)
Derivable Data
41(1)
Determining the Primary Key
41(2)
Repeating Groups and First Normal Form
43(4)
Limit on Maximum Number of Occurrences
43(1)
Data Reusability and Program Complexity
43(1)
Recognizing Repeating Groups
44(1)
Removing Repeating Groups
45(1)
Determining the Primary Key of the New Table
46(1)
First Normal Form
47(1)
Second and Third Normal Forms
47(6)
Problems with Tables in First Normal Form
47(1)
Eliminating Redundancy
48(1)
Determinants
48(3)
Third Normal Form
51(2)
Definitions and a Few Refinements
53(7)
Determinants and Functional Dependency
53(1)
Primary Keys
54(1)
Candidate Keys
54(1)
A More Formal Definition of Third Normal Form
55(1)
Foreign Keys
55(1)
Referential Integrity
56(1)
Update Anomalies
57(1)
Denormalization and Unnormalization
58(1)
Column and Table Names
59(1)
Choice, Creativity, and Normalization
60(2)
Terminology
62(1)
Summary
63(2)
The Entity-Relationship Approach
65(46)
Introduction
65(1)
A Diagrammatic Representation
65(7)
The Basic Symbols: Boxes and Arrows
66(1)
Diagrammatic Representation of Foreign Keys
67(1)
Interpreting the Diagram
68(1)
Optionality
69(1)
Verifying the Model
70(1)
Redundant Arrows
71(1)
The Top-Down Approach: Entity-Relationship Modeling
72(4)
Developing the Diagram Top Down
74(1)
Terminology
75(1)
Entity Classes
76(6)
Entity Diagramming Convention
77(1)
Entity Class Naming
78(2)
Entity Class Definitions
80(2)
Relationships
82(22)
Relationship Diagramming Conventions
82(5)
Many-to-Many Relationships
87(5)
One-to-One Relationships
92(1)
Self-Referencing Relationships
93(3)
Relationships Involving Three or More Entity Classes
96(2)
Transferability
98(4)
Dependent and Independent Entity Classes
102(1)
Relationship Names
103(1)
Attributes
104(1)
Attribute Identification and Definition
104(1)
Primary Keys and the Conceptual Model
105(1)
Myths and Folklore
105(1)
Entity Classes without Relationships
106(1)
Allowed Combinations of Cardinality and Optionality
106(1)
Creativity and E-R Modeling
106(3)
Summary
109(2)
Subtypes and Supertypes
111(34)
Introduction
111(1)
Different Levels of Generalization
111(2)
Rules versus Stability
113(2)
Using Subtypes and Supertypes
115(1)
Subtypes and Supertypes as Entity Classes
116(1)
Naming Subtypes
117(1)
Diagramming Conventions
117(2)
Boxes in Boxes
117(1)
UML Conventions
118(1)
Using Tools That Do Not Support Subtyping
119(1)
Definitions
119(1)
Attributes of Supertypes and Subtypes
119(1)
Nonoverlapping and Exhaustive
120(3)
Overlapping Subtypes and Roles
123(4)
Ignoring Real-World Overlaps
123(1)
Modeling Only the Supertype
124(1)
Modeling the Roles as Participation in Relationships
124(1)
Using Role Entity Classes and One-to-One Relationships
125(1)
Multiple Partitions
126(1)
Hierarchy of Subtypes
127(1)
Benefits of Using Subtypes and Supertypes
128(6)
Creativity
129(1)
Presentation: Level of Detail
129(1)
Communication
130(2)
Input to the Design of Views
132(1)
Classifying Common Patterns
132(1)
Divide and Conquer
133(1)
When Do We Stop Supertyping and Subtyping?
134(4)
Differences in Identifiers
134(1)
Different Attribute Groups
135(1)
Different Relationships
135(1)
Different Processes
136(1)
Migration from One Subtype to Another
136(1)
Communication
136(1)
Capturing Meaning and Rules
137(1)
Summary
137(1)
Generalization of Relationships
138(4)
Generalizing Several One-to-Many Relationships to a Single Many-to-Many Relationship
138(1)
Generalizing Several One-to-Many Relationships to a Single One-to-Many Relationship
139(2)
Generalizing One-to-Many and Many-to-Many Relationships
141(1)
Theoretical Background
142(1)
Summary
143(2)
Attributes and Columns
145(38)
Introduction
145(1)
Attribute Definition
146(1)
Attribute Disaggregation: One Fact per Attribute
147(5)
Simple Aggregation
148(2)
Conflated Codes
150(1)
Meaningful Ranges
151(1)
Inappropriate Generalization
151(1)
Types of Attributes
152(14)
DBMS Datatypes
152(2)
The Attribute Taxonomy in Detail
154(4)
Attribute Domains
158(4)
Column Datatype and Length Requirements
162(4)
Conversion Between External and Internal Representations
166(1)
Attribute Names
166(5)
Objectives of Standardizing Attribute Names
166(2)
Some Guidelines for Attribute Naming
168(3)
Attribute Generalization
171(9)
Options and Trade-Offs
171(1)
Attribute Generalization Resulting from Entity Generalization
172(1)
Attribute Generalization within Entity Classes
173(4)
``First Among Equals''
177(1)
Limits to Attribute Generalization
178(2)
Summary
180(3)
Primary Keys and Identity
183(24)
Basic Requirements and Trade-Offs
183(2)
Basic Technical Criteria
185(6)
Applicability
185(1)
Uniqueness
186(2)
Minimality
188(1)
Stability
189(2)
Surrogate Keys
191(3)
Performance and Programming Issues
191(1)
Matching Real-World Identifiers
191(1)
Should Surrogate Keys Be Visible?
192(1)
Subtypes and Surrogate Keys
193(1)
Structured Keys
194(7)
When to Use Structured Keys
196(1)
Programming and Structured Keys
197(1)
Performance Issues with Structured Keys
198(1)
Running Out of Numbers
199(2)
Multiple Candidate Keys
201(1)
Choosing a Primary Key
201(1)
Normalization Issues
201(1)
Guidelines for Choosing Keys
202(2)
Tables Implementing Independent Entity Classes
202(1)
Tables Implementing Dependent Entity Classes and Many-to-Many Relationships
203(1)
Partially-Null Keys
204(2)
Summary
206(1)
Extensions and Alternatives
207(22)
Introduction
207(2)
Extensions to the Basic E-R Approach
209(7)
Introduction
209(1)
Advanced Attribute Concepts
210(6)
The Chen E-R Approach
216(4)
The Basic Conventions
216(1)
Relationships with Attributes
217(1)
Relationships Involving Three or More Entity Classes
217(1)
Roles
218(1)
The Weak Entity Concept
219(1)
Chen Conventions in Practice
220(1)
Using UML Object Class Diagrams
220(7)
A Conceptual Data Model in UML
221(1)
Advantages of UML
222(5)
Object Role Modeling
227(1)
Summary
228(1)
Part II Putting It Together
229(160)
Organizing the Data Modeling Task
231(20)
Data Modeling in the Real World
231(2)
Key Issues in Project Organization
233(5)
Recognition of Data Modeling
233(1)
Clear Use of the Data Model
234(1)
Access to Users and Other Business Stakeholders
234(1)
Conceptual, Logical, and Physical Models
235(1)
Cross-Checking with the Process Model
236(1)
Appropriate Tools
237(1)
Roles and Responsibilities
238(2)
Partitioning Large Projects
240(2)
Maintaining the Model
242(6)
Examples of Complex Changes
242(5)
Managing Change in the Modeling Process
247(1)
Packaging It Up
248(1)
Summary
249(2)
The Business Requirements
251(22)
Purpose of the Requirements Phase
251(2)
The Business Case
253(1)
Interviews and Workshops
254(4)
Should You Model in Interviews and Workshops?
255(1)
Interviews with Senior Managers
256(1)
Interviews with Subject Matter Experts
257(1)
Facilitated Workshops
257(1)
Riding the Trucks
258(1)
Existing Systems and Reverse Engineering
259(2)
Process Models
261(1)
Object Class Hierarchies
261(9)
Classifying Object Classes
263(2)
A Typical Set of Top-Level Object Classes
265(2)
Developing an Object Class Hierarchy
267(3)
Potential Issues
270(1)
Advantages of the Object Class Hierarchy Technique
270(1)
Summary
270(3)
Conceptual Data Modeling
273(48)
Designing Real Models
273(2)
Learning from Designers in Other Disciplines
275(1)
Starting the Modeling
276(1)
Patterns and Generic Models
277(8)
Using Patterns
277(1)
Using a Generic Model
278(1)
Adapting Generic Models from Other Applications
279(3)
Developing a Generic Model
282(2)
When There Is Not a Generic Model
284(1)
Bottom-Up Modeling
285(3)
Top-Down Modeling
288(1)
When the Problem Is Too Complex
288(2)
Hierarchies, Networks, and Chains
290(5)
Hierarchies
291(2)
Networks (Many-to-Many Relationships)
293(2)
Chains (One-to-One Relationships)
295(1)
One-to-One Relationships
295(5)
Distinct Real-World Concepts
296(1)
Separating Attribute Groups
297(1)
Transferable One-to-One Relationships
298(1)
Self-Referencing One-to-One Relationships
299(1)
Support for Creativity
299(1)
Developing Entity Class Definitions
300(1)
Handling Exceptions
301(1)
The Right Attitude
302(3)
Being Aware
303(1)
Being Creative
303(1)
Analyzing or Designing
303(1)
Being Brave
304(1)
Being Understanding and Understood
304(1)
Evaluating the Model
305(1)
Direct Review of Data Model Diagrams
306(2)
Comparison with the Process Model
308(1)
Testing the Model with Sample Data
308(1)
Prototypes
309(1)
The Assertions Approach
309(10)
Naming Conventions
310(1)
Rules for Generating Assertions
311(8)
Summary
319(2)
Logical Database Design
321(38)
Introduction
321(1)
Overview of the Transformations Required
322(3)
Table Specification
325(9)
The Standard Transformation
325(1)
Exclusion of Entity Classes from the Database
325(1)
Classification Entity Classes
325(1)
Many-to-Many Relationship Implementation
326(2)
Relationships Involving More Than Two Entity Classes
328(1)
Supertype/Subtype Implementation
328(6)
Basic Column Definition
334(7)
Attribute Implementation: The Standard Transformation
334(1)
Category Attribute Implementation
335(1)
Derivable Attributes
336(1)
Attributes of Relationships
336(1)
Complex Attributes
337(1)
Multivalued Attribute Implementation
337(2)
Additional Columns
339(1)
Column Datatypes
340(1)
Column Nullability
340(1)
Primary Key Specification
341(1)
Foreign Key Specification
342(12)
One-to-Many Relationship Implementation
343(3)
One-to-One Relationship Implementation
346(1)
Derivable Relationships
347(1)
Optional Relationships
348(2)
Overlapping Foreign Keys
350(2)
Split Foreign Keys
352(2)
Table and Column Names
354(1)
Logical Data Model Notations
355(2)
Summary
357(2)
Physical Database Design
359(30)
Introduction
359(2)
Inputs to Database Design
361(1)
Options Available to the Database Designer
362(1)
Design Decisions Which Do Not Affect Program Logic
363(9)
Indexes
363(7)
Data Storage
370(2)
Memory Usage
372(1)
Crafting Queries to Run Faster
372(2)
Locking
373(1)
Logical Schema Decisions
374(10)
Alternative Implementation of Relationships
374(1)
Table Splitting
374(2)
Table Merging
376(1)
Duplication
377(1)
Denormalization
378(1)
Ranges
379(1)
Hierarchies
380(2)
Integer Storage of Dates and Times
382(1)
Additional Tables
383(1)
Views
384(2)
Views of Supertypes and Subtypes
385(1)
Inclusion of Derived Attributes in Views
385(1)
Denormalization and Views
385(1)
Views of Split and Merged Tables
386(1)
Summary
386(3)
Part III Advanced Topics
389(130)
Advanced Normalization
391(26)
Introduction
391(1)
Introduction to the Higher Normal Forms
392(2)
Common Misconceptions
392(2)
Boyce-Codd Normal Form
394(4)
Example of Structure in 3NF but not in BCNF
394(2)
Definition of BCNF
396(1)
Enforcement of Rules versus BCNF
397(1)
A Note on Domain Key Normal Form
398(1)
Fourth Normal Form (4NF) and Fifth Normal Form (5NF)
398(9)
Data in BCNF but not in 4NF
399(2)
Fifth Normal Form (5NF)
401(3)
Recognizing 4NF and 5NF Situations
404(1)
Checking for 4NF and 5NF with the Business Specialist
405(2)
Beyond 5NF: Splitting Tables Based on Candidate Keys
407(1)
Other Normalization Issues
408(7)
Normalization and Redundancy
408(2)
Reference Tables Produced by Normalization
410(1)
Selecting the Primary Key after Removing Repeating Groups
411(3)
Sequence of Normalization and Cross-Table Anomalies
414(1)
Advanced Normalization in Perspective
415(1)
Summary
416(1)
Modeling Business Rules
417(34)
Introduction
417(1)
Types of Business Rules
418(2)
Data Rules
418(2)
Process Rules
420(1)
What Rules are Relevant to the Data Modeler?
420(1)
Discovery and Verification of Business Rules
420(2)
Cardinality Rules
420(1)
Other Data Validation Rules
421(1)
Data Derivation Rules
421(1)
Documentation of Business Rules
422(5)
Documentation in an E-R Diagram
422(1)
Documenting Other Rules
422(2)
Use of Subtypes to Document Rules
424(3)
Implementing Business Rules
427(19)
Where to Implement Particular Rules
428(5)
Implementation Options: A Detailed Example
433(3)
Implementing Mandatory Relationships
436(2)
Referential Integrity
438(1)
Restricting an Attribute to a Discrete Set of Values
439(3)
Rules Involving Multiple Attributes
442(1)
Recording Data That Supports Rules
442(1)
Rules That May Be Broken
443(2)
Enforcement of Rules Through Primary Key Selection
445(1)
Rules on Recursive Relationships
446(4)
Types of Rules on Recursive Relationships
447(2)
Documenting Rules on Recursive Relationships
449(1)
Implementing Constraints on Recursive Relationships
449(1)
Analogous Rules in Many-to-Many Relationships
450(1)
Summary
450(1)
Time-Dependent Data
451(24)
The Problem
451(1)
When Do We Add the Time Dimensions?
452(1)
Audit Trails and Snapshots
452(10)
The Basic Audit Trail Approach
453(5)
Handling Nonnumeric Data
458(1)
The Basic Snapshot Approach
458(4)
Sequences and Verisions
462(1)
Handling Deletions
463(1)
Archiving
463(1)
Modeling Time-Dependent Relationships
464(5)
One-to-Many Relationships
464(2)
Many-to-Many Relationships
466(2)
Self-Referencing Relationships
468(1)
Date Tables
469(1)
Temporal Business Rules
469(4)
Changes to the Data Structure
473(1)
Putting It into Practice
473(1)
Summary
474(1)
Modeling for Data Warehouses and Data Marts
475(24)
Introduction
475(3)
Characteristics of Data Warehouses and Data Marts
478(2)
Data Integration: Working with Existing Databases
478(1)
Loads Rather Than Updates
478(1)
Less Predictable Database ``Hits''
479(1)
Complex Queries---Simple Interface
479(1)
History
480(1)
Summarization
480(1)
Quality Criteria for Warehouse and Mart Models
480(3)
Completeness
480(1)
Nonredundancy
481(1)
Enforcement of Business Rules
482(1)
Data Reusability
482(1)
Stability and Flexibility
482(1)
Simplicity and Elegance
483(1)
Communication Effectiveness
483(1)
Performance
483(1)
The Basic Design Principle
483(1)
Modeling for the Data Warehouse
484(4)
An Initial Model
484(1)
Understanding Existing Data
485(1)
Determining Requirements
485(1)
Determining Sources and Dealing with Differences
485(2)
Shaping Data for Data Marts
487(1)
Modeling for the Data Mart
488(8)
The Basic Challenge
488(1)
Multidimensional Databases, Stars and Snowflakes
488(6)
Modeling Time-Dependent Data
494(2)
Summary
496(3)
Enterprise Data Models and Data Management
499(20)
Introduction
499(1)
Data Management
500(3)
Problems of Data Mismanagement
500(1)
Managing Data as a Shared Resource
501(1)
The Evolution of Data Management
501(2)
Classification of Existing Data
503(1)
A Target for Planning
504(2)
A Context for Specifying New Databases
506(2)
Determining Scope and Interfaces
506(1)
Incorporating the Enterprise Data Model in the Development Life Cycle
506(2)
Guidance for Database Design
508(1)
Input to Business Planning
508(1)
Specification of an Enterprise Database
509(2)
Characteristics of Enterprise Data Models
511(1)
Developing an Enterprise Data Model
512(4)
The Development Cycle
512(1)
Partitioning the Task
513(1)
Inputs to the Task
514(1)
Expertise Requirements
515(1)
External Standards
515(1)
Choice, Creativity, and Enterprise Data Models
516(1)
Summary
517(2)
Further Reading 519


Graeme C. Simsion has over 25 years experience in information systems as a DBA, data modeling consultant, business systems designer, manager, and researcher. He is a regular presenter at industry and academic forums, and is currently a Senior Fellow with the Department of Information Systems at the University of Melbourne. Graham C. Witt is an independent consultant with over 30 years of experience in assisting enterprises to acquire relevant and effective IT solutions. His clients include major banks and other financial institutions; businesses in the insurance, utilities, transport and telecommunications sectors; and a wide variety of government agencies. A former guest lecturer on Database Systems at University of Melbourne, he is a frequent presenter at international data management conferences.