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9783540653653

Fundamentals of Data Warehouses

by ; ; ; ;
  • ISBN13:

    9783540653653

  • ISBN10:

    3540653651

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2000-01-01
  • Publisher: Springer Verlag
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Summary

This book presents the first comparative review of the state-of-the-art and the best current practices of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, metadata management, quality assessment, and design optimization. A conceptual framework is presented by which the architecture and quality of a data warehouse can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence.

Table of Contents

Data Warehouse Practice: An Overview
1(14)
Data Warehouse Components
2(3)
Designing the Data Warehouse
5(1)
Getting Heterogeneous Data Into the Warehouse
5(1)
Getting Multidimensional Data Out of the Warehouse
6(4)
Physical Structure of Data Warehouses
10(2)
Metadata Management
12(3)
Data Warehouse Research: Issues and Projects
15(12)
Data Extraction and Reconciliation
15(1)
Data Aggregation and Customization
15(1)
Query Optimization
16(1)
Update Propagation
17(1)
Modeling and Measuring Data Warehouse Quality
17(2)
Some Research Projects in Data Warehousing
19(2)
Three Perspectives of Data Warehouse Metadata
21(6)
Source Integration
27(20)
The Practice of Source Integration
27(3)
Tools for Data Warehouse Management
28(1)
Tools for Data Integration
29(1)
Research in Source Integration
30(10)
Schema Integration
32(1)
Preintegration
33(1)
Schema Comparison
34(1)
Schema Conforming
35(1)
Schema Merging and Restructuring
36(1)
Data Integration-Virtual
36(1)
Carnot
36(1)
SIMS
37(1)
Information Manifold
37(1)
TSIMMIS
38(1)
Data Integration-Materialized
39(1)
Squirrel
39(1)
WHIPS
40(1)
Towards Systematic Methodologies for Source Integration
40(5)
Architecture for Source Integration
41(1)
Conceptual Perspective
42(1)
Logical Perspective
42(1)
Mappings
43(1)
Methodology for Source Integration
43(1)
Source-Driven Integration
43(2)
Client-Driven Integration
45(1)
Concluding Remarks
45(2)
Data Warehouse Refreshment
47(40)
What is Data Warehouse Refreshment?
47(7)
Refreshment Process within the Data Warehouse Lifecycle
47(3)
Requirements and Difficulties of Data Warehouse Refreshment
50(2)
Data Warehouse Refreshment: Problem Statement
52(2)
Incremental Data Extraction
54(8)
Wrapper Functionality
55(1)
Change Monitoring
56(2)
Snapshot Sources
58(1)
Specific Sources
59(1)
Logged Sources
59(1)
Queryable Sources
59(1)
Replicated Sources
60(1)
Callback Sources
60(1)
Internal Action Sources
61(1)
Data Cleaning
62(6)
Conversion and Normalization Functions
63(1)
Special-purpose Cleaning
64(1)
Domain-independent Cleaning
64(1)
Rule-based Cleaning
65(1)
User-Specified Rules
65(1)
Automatically-derived Rules
66(1)
Concluding Remarks on Data Cleaning
67(1)
Update Propagation into Materialized Views
68(5)
Notations and Definitions
68(1)
View Maintenance: General Results
69(1)
Characterizing (Self) Maintainable Views
69(1)
Optimization
69(1)
Joint Maintenance of a Set of Views
70(1)
Evaluating View Maintenance Algorithms
71(1)
View Maintenance in Data Warehouses-Specific Results
72(1)
Consistency
72(1)
Optimization
73(1)
Temporal Data Warehouses
73(1)
Toward a Quality-Oriented Refreshment Process
73(11)
Quality Analysis for Refreshment
74(1)
Quality Dimensions
74(1)
Quality Factors
75(1)
Design Choices
75(1)
Links between Quality Factors and Design Choices
76(1)
Implementing the Refreshment Process
77(1)
Planning the Refreshment Process
77(3)
Workflow Modeling with Rules
80(2)
Main Features of the Toolkit
82(1)
Functional Architecture of the Active Refreshment System
82(2)
Concluding Remarks
84(3)
Multidimensional Data Models and Aggregation
87(20)
Multidimensional View of Information
90(2)
ROLAP Data Model
92(4)
MOLAP Data Model
96(1)
Logical Models for Multidimensional Information
97(3)
Conceptual Models for Multidimensional Information
100(5)
Inference Problems for Multidimensional Conceptual Modeling
102(1)
Which Formal Framework to Choose?
103(2)
Conclusions
105(2)
Query Processing and Optimization
107(16)
Description and Requirements for Data Warehouse Queries
107(6)
Queries at the Back End
108(1)
Queries at the Front End
108(1)
Queries in the Core
109(1)
Transactional vs. Data Warehouse Queries
109(1)
Canned Queries vs. Ad-hoc Queries
110(1)
Multidimensional Queries
110(1)
Querying the Dimensions
111(1)
Querying Factual Information
111(1)
Extensions of SQL
112(1)
Query Processing Techniques
113(8)
Data Access
113(1)
Indexes
113(1)
Aggregate Query Processing with Indexes
114(1)
Join-Indexes for Stars
115(1)
The Extended Datacube Model
115(1)
Evaluation Strategies
116(1)
Interleaving Group-By and Join
116(1)
Optimization of Nested Subqueries
117(1)
Exploitation of Redundancy
117(1)
Which Views are Useful for Answering a Query?
118(3)
What is the Expected Size of an Aggregate View?
121(1)
Conclusions and Research Directions
121(2)
Metadata and Data Warehouse Quality
123(36)
Metadata Management in Data Warehouse Practice
124(4)
Meta Data Interchange Specification (MDIS)
125(1)
The Telos Language
125(2)
Microsoft Repository
127(1)
A Repository Model for the DWQ Framework
128(8)
Conceptual Perspective
130(1)
Logical Perspective
130(1)
Physical Perspective
131(1)
Applying the Architecture Model
131(5)
Defining Data Warehouse Quality
136(6)
Data Quality
136(1)
Stakeholders and Goals in Data Warehouse Quality
137(3)
State of Practice in Data Warehouse Quality
140(2)
Managing Data Warehouse Quality
142(10)
Quality Function Deployment
142(1)
The Need for Richer Quality Models: An Example
143(1)
The Goal-Question-Metric Approach
144(2)
Repository Support for the GQM Approach
146(1)
The Quality Meta Model
147(2)
Implementation Support for the Quality Meta Model
149(2)
Understanding, Controlling and Improving Quality with the Repository
151(1)
Towards Quality-Driven Data Warehouse Design
152(5)
Linking Quality Factors to Warehouse Components
152(1)
An Example: Optimizing the Materialization of DW Views
153(4)
Conclusions
157(2)
References 159(20)
Appendix A. ISO Standards Information Quality 179(4)
Appendix B. Glossary 183(8)
B.1 Data Warehouse Systems
183(1)
B.2 Data Quality
184(2)
B.3 Source Integration
186(1)
B.4 Multidimensional Aggregation
187(2)
B.5 Query Optimization
189(2)
Index 191

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