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E-raamat: Developing High Quality Data Models

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(Director of Information Junction, UK)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 07-Feb-2011
  • Kirjastus: Morgan Kaufmann Publishers In
  • Keel: eng
  • ISBN-13: 9780123751072
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Developing High Quality Data ModelsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 07-Feb-2011
  • Kirjastus: Morgan Kaufmann Publishers In
  • Keel: eng
  • ISBN-13: 9780123751072
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Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models.

The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool.

This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling.

Arvustused

"This guide to developing high quality data models provides practical instruction in understanding the core principle of data modeling and creating accurate models from complex databases. The work is divided into four sections covering the basics of data model types and uses, general principles for data model components and an ontological framework for consistent data models. A final section presents a complete, standards compliant data model created with the Jotne EPM Technology EDMVisusalExpress data modeling tool. Numerous illustrations, charts and sample programming code are included throughout the work and access to additional online content, including the sample data model, is provided. West is an experienced data modeler working in the energy field." --Book News, Reference & Research

"Overall, the book is a helpful guide for those who wish to go deep into the art of developing high quality data models. Readers will appreciate: how West connects data models with EA and business processes; the ontological approach, which offers a framework for formal, generic, and consistent models; the efficient use of diagrams for explaining the notions; and the philosophical concepts discussed throughout the text. The book is highly technical. Although it does not directly address people from academia, it will be very useful for related courses, especially those that deal with IT and business processes. Finally, the book highlights the importance of quality in data modeling for decision making." --Computing reviews.com

Muu info

The path to high quality data modeling begins with Developing High Quality Data Models.
Preface xi
PART 1 MOTIVATIONS AND NOTATIONS
Chapter 1 Introduction
3(6)
1.1 Some Questions about Data Models
3(1)
1.2 Purpose
4(1)
1.3 Target Audience
5(1)
1.4 What is a Data Model?
5(1)
1.5 Why Do We Do Data Models?
5(2)
1.6 Approach to Data Modeling
7(1)
1.7 Structure of This Book
7(2)
Chapter 2 Entity Relationship Model Basics
9(14)
2.1 Oh, It's Boxes and Lines Again
9(1)
2.2 Graphical or Lexical
10(1)
2.3 Graphical Notations: Complexity vs. Understandability vs. Capability
11(1)
2.4 Language and Notation Elements
12(1)
2.5 Express-G
13(7)
2.6 Notation for Instances and Classes
20(1)
2.7 Layout of Data Models
21(1)
2.8 Reflections
22(1)
Chapter 3 Some Types and Uses of Data Models
23(14)
3.1 Different Types of Data Models
23(6)
3.2 Integration of Data and Data Models
29(7)
3.3 Concluding Remarks
36(1)
Chapter 4 Data Models and Enterprise Architecture
37(14)
4.1 The Business Process Model
40(1)
4.2 Information Architecture
40(1)
4.3 Information Operations
41(1)
4.4 Organization
42(1)
4.5 Methodologies and Standards
42(1)
4.6 Management
43(1)
4.7 Wider Infrastructure
44(1)
4.8 Enterprise Architecture Mappings
44(2)
4.9 The Process/Data Balance
46(5)
Chapter 5 Some Observations on Data Models and Data Modeling
51(12)
5.1 Limitations of Data Models
51(4)
5.2 Challenges in Data Modeling
55(8)
PART 2 GENERAL PRINCIPLES FOR DATA MODELS
Chapter 6 Some General Principles for Conceptual, Integration, and Enterprise Data Models
63(16)
6.1 Data Modeling Approach
64(4)
6.2 General Principles
68(6)
6.3 Understanding Relationships
74(3)
6.4 Principles for Data Models
77(1)
6.5 Naughtiness Index
78(1)
Chapter 7 Applying the Principles for Attributes
79(10)
7.1 Looking for Attributes Representing Relationships
79(5)
7.2 Identifiers
84(4)
7.3 What Other Attributes Might You Expect?
88(1)
7.4 Concluding Remarks on Attributes
88(1)
Chapter 8 General Principles for Relationships
89(6)
8.1 Example of Inappropriate Cardinalities---Batch and Product Type
89(2)
8.2 Example of Inappropriate Cardinalities---Packed Products
91(1)
8.3 An Example of Inappropriate Cardinalities---Ship
92(1)
8.4 A Good Example of Applying the Principles for Relationships---Transfer and Storage
93(1)
8.5 Concluding Remarks
94(1)
Chapter 9 General---Principles for Entity Types
95(12)
9.1 An Example---Combined Entity Types
95(2)
9.2 An Example---Stock
97(1)
9.3 Getting Subtypes Wrong
98(2)
9.4 An Example of Fixed Hierarchies---Stock Classification
100(3)
9.5 Getting the Right Level of Abstraction
103(1)
9.6 Impact of Using the Principles
104(3)
PART 3 AN ONTOLOGICAL FRAMEWORK FOR CONSISTENT DATA MODELS
Chapter 10 Motivation and Overview for an Ontological Framework
107(16)
10.1 Motivation
107(1)
10.2 Ontological Foundation
108(11)
10.3 A Data Model for the Ontological Foundations
119(2)
10.4 Closing Remarks
121(2)
Chapter 11 Spatio-Temporal Extents
123(14)
11.1 Parts
123(1)
11.2 Individuals and States
124(3)
11.3 Inheritance of Properties by Substates
127(1)
11.4 Space and Time
127(1)
11.5 Ordinary Physical Objects
128(1)
11.6 Levels of Reality
129(2)
11.7 Activities and Events
131(2)
11.8 Associations
133(2)
11.9 A Data Model for Individuals
135(2)
Chapter 12 Classes
137(14)
12.1 What Is a Set?
137(1)
12.2 Sets and Four-Dimensionalism
138(1)
12.3 Some Different Kinds of Set Theory
139(4)
12.4 A High Level Data Model for Classes
143(1)
12.5 Properties and Quantities
144(2)
12.6 Scales and Units
146(2)
12.7 Kinds
148(1)
12.8 Concluding Remarks
149(2)
Chapter 13 Intentionally Constructed Objects
151(24)
13.1 Introduction
151(1)
13.2 Functional Objects
152(2)
13.3 Socially Constructed Objects
154(1)
13.4 Ownership
155(2)
13.5 Agreements
157(2)
13.6 Contracts
159(2)
13.7 Organizations
161(3)
13.8 Product
164(4)
13.9 Representation
168(4)
13.10 Concluding Remarks
172(3)
Chapter 14 Systems and System Components
175(12)
14.1 What Are Systems and System Components?
175(2)
14.2 The Nature of System Components
177(3)
14.3 Another Example: A Football Match
180(2)
14.4 Similarities, Differences, and Relationships to Other Things
182(1)
14.5 Do I Need a Separate Set of Classes for System Components?
183(1)
14.6 Extending the Framework for System and System Component
183(3)
14.7 Concluding Remarks
186(1)
Chapter 15 Requirements Specification
187(10)
15.1 A Process for Procurement
187(4)
15.2 Requirements Specification
191(6)
Chapter 16 Concluding Remarks
197(4)
PART 4 THE HQDM FRAMEWORK SCHEMA
Chapter 17 Hqdm_Framework
201(146)
17.1 Thing and Abstract Object
202(2)
17.2 Class and Class of Class
204(3)
17.3 Relationship and Class of Relationship
207(4)
17.4 Spatio-Temporal Extent and Class of Spatio-Temporal Extent
211(5)
17.5 Event, Class of Event, and Point in Time
216(2)
17.6 State and Individual
218(2)
17.7 Physical Object
220(3)
17.8 Ordinary Physical Object
223(3)
17.9 Kind of Individual and Subtypes
226(4)
17.10 Kind of System and System Component
230(3)
17.11 Period of Time and Possible Worlds
233(2)
17.12 Physical Properties and Physical Quantities
235(6)
17.13 Association
241(2)
17.14 Activity
243(2)
17.15 Participant
245(2)
17.16 Role, Class of Activity, and Class of Association
247(4)
17.17 System
251(3)
17.18 System Component
254(2)
17.19 Installed Object
256(1)
17.20 Biological Object
257(3)
17.21 Ordinary Biological Object
260(3)
17.22 Biological System
263(2)
17.23 Person
265(3)
17.24 Biological System Component
268(4)
17.25 Intentionally Constructed Object
272(2)
17.26 Functional Object
274(3)
17.27 Ordinary Functional Object
277(3)
17.28 Functional System
280(6)
17.29 Socially Constructed Object
286(4)
17.30 Party
290(2)
17.31 Organization and Language Community
292(4)
17.32 Employment
296(2)
17.33 Organization Component and Position
298(6)
17.34 Money
304(3)
17.35 Ownership
307(3)
17.36 Transfer of Ownership
310(4)
17.37 Socially Constructed Activity
314(1)
17.38 Class of Socially Constructed Activity
315(1)
17.39 Agreement
316(4)
17.40 Contract
320(5)
17.41 Offer and Acceptance of Offer
325(1)
17.42 Sale of Goods
326(5)
17.43 Sales Product, Product Brand, and Sales Product Version
331(3)
17.44 Offering
334(3)
17.45 Sign and Pattern
337(6)
17.46 Requirement and Requirement Specification
343(4)
Appendix: A Mapping between the HQDM Schema and ISO 15926-2 347(32)
Index 379
Matthew West spent over 20 years as a leading data modeler for Shell where he was a key technical contributor to data modeling and data management standards and their application. Matthew was responsible for Shell's Downstream Data Model. He currently serves as the Director of Information Junction, a data architecture and analysis consultancy in the UK. He is also a key contributor to ISO 15926 (Lifecycle integration of process data) and ISO 8000 (Data and Information Quality). Matthew is a Visiting Professor at the University of Leeds
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