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Contents
4.1.4
Data Warehousing: A Multitiered Architecture
130
4.1.5
Data Warehouse Models: Enterprise Warehouse, Data Mart,
and Virtual Warehouse
132
4.1.6
Extraction, Transformation, and Loading
134
4.1.7
Metadata Repository
134
4.2
Data Warehouse Modeling: Data Cube and OLAP
135
4.2.1
Data Cube: A Multidimensional Data Model
136
4.2.2
Stars, Snowflakes, and Fact Constellations: Schemas
for Multidimensional Data Models
139
4.2.3
Dimensions: The Role of Concept Hierarchies
142
4.2.4
Measures: Their Categorization and Computation
144
4.2.5
Typical OLAP Operations
146
4.2.6
A Starnet Query Model for Querying Multidimensional
Databases
149
4.3
Data Warehouse Design and Usage
150
4.3.1
A Business Analysis Framework for Data Warehouse Design
150
4.3.2
Data Warehouse Design Process
151
4.3.3
Data Warehouse Usage for Information Processing
153
4.3.4
From Online Analytical Processing to Multidimensional
Data Mining
155
4.4
Data Warehouse Implementation
156
4.4.1
Efficient Data Cube Computation: An Overview
156
4.4.2
Indexing OLAP Data: Bitmap Index and Join Index
160
4.4.3
Efficient Processing of OLAP Queries
163
4.4.4
OLAP Server Architectures: ROLAP versus MOLAP
versus HOLAP
164
4.5
Data Generalization by Attribute-Oriented Induction
166
4.5.1
Attribute-Oriented Induction for Data Characterization
167
4.5.2
Efficient Implementation of Attribute-Oriented Induction
172
4.5.3
Attribute-Oriented Induction for Class Comparisons
175
4.6
Summary__178'>Summary
178
4.7
Exercises
180
4.8
Bibliographic Notes
184
Chapter 5 Data Cube Technology
187
5.1
Data Cube Computation: Preliminary Concepts
188
5.1.1
Cube Materialization: Full Cube, Iceberg Cube, Closed Cube,
and Cube Shell
188
5.1.2
General Strategies for Data Cube Computation
192
5.2
Data Cube Computation Methods
194
5.2.1
Multiway Array Aggregation for Full Cube Computation
195
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5.2.2
BUC: Computing Iceberg Cubes from the Apex Cuboid
Downward
200
5.2.3
Star-Cubing: Computing Iceberg Cubes Using a Dynamic
Star-Tree Structure
204
5.2.4
Precomputing Shell Fragments for Fast High-Dimensional OLAP
210
5.3
Processing Advanced Kinds of Queries by Exploring Cube
Technology
218
5.3.1
Sampling Cubes: OLAP-Based Mining on Sampling Data
218
5.3.2
Ranking Cubes: Efficient Computation of Top-k Queries
225
5.4
Multidimensional Data Analysis in Cube Space
227
5.4.1
Prediction Cubes: Prediction Mining in Cube Space
227
5.4.2
Multifeature Cubes: Complex Aggregation at Multiple
Granularities
230
5.4.3
Exception-Based, Discovery-Driven Cube Space Exploration
231
5.5
Summary
234
5.6
Exercises
235
5.7
Bibliographic Notes
240
Chapter 6 Mining Frequent Patterns, Associations, and Correlations: Basic
Concepts and Methods
243
6.1
Basic Concepts
243
6.1.1
Market Basket Analysis: A Motivating Example
244
6.1.2
Frequent Itemsets, Closed Itemsets, and Association Rules
246
6.2
Frequent Itemset Mining Methods
248
6.2.1
Apriori Algorithm: Finding Frequent Itemsets by Confined
Candidate Generation
248
6.2.2
Generating Association Rules from Frequent Itemsets
254
6.2.3
Improving the Efficiency of Apriori
254
6.2.4
A Pattern-Growth Approach for Mining Frequent Itemsets
257
6.2.5
Mining Frequent Itemsets Using Vertical Data Format
259
6.2.6
Mining Closed and Max Patterns
262
6.3
Which Patterns Are Interesting?—Pattern Evaluation
Methods
264
6.3.1
Strong Rules Are Not Necessarily Interesting
264
6.3.2
From Association Analysis to Correlation Analysis
265
6.3.3
A Comparison of Pattern Evaluation Measures
267
6.4
Summary
271
6.5
Exercises
273
6.6
Bibliographic Notes
276
