Index structures for data warehouses

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Index structures for data warehouses / Marcus Jürgens

資料種別:: 図書
出版情報:: Berlin : Springer, c2002
形態:: x, 132 p. ; 24 cm
シリーズ名:: Lecture notes in computer science ; 1859 <BA00009279>
著者名:: Jürgens, Marcus
ISBN:: 9783540433682 [3540433686]
書誌ID:: BA56295004

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注記:

Includes bibliographical references ( p. [125]-130) and index

主題:

器械計算．計算器

言語:

英語

目次情報:

Introduction / 1：

Goals / 1.1：

Outline / 1.2：

State of the Art of Data Warehouse Research / 2：

Traditional Transaction-Oriented Systems / 2.1：

Data Warehouses for Decision Support / 2.3：

OLAP Vs. OLTP / 2.4：

Accelerating Query Speed / 2.5：

Denormalized Schemas / 2.5.1：

Materialized Views / 2.5.2：

No Locking / 2.5.3：

On-line Aggregation / 2.5.4：

Index Structures / 2.5.5：

Summary / 2.6：

Data Storage and Index Structures / 3：

Memory Hierarchy / 3.1：

Mechanics of Disks / 3.3：

Data Space and Queries / 3.4：

Data Space / 3.4.1：

Queries / 3.4.2：

Tree-Based Indexing / 3.5：

Top-Down, Bottom-Up, and Bulk Loading / 3.5.1：

Point Quadtrees / 3.5.2：

kd-tree / 3.5.3：

kdb-tree / 3.5.4：

R-tree / 3.5.5：

R*-tree / 3.5.6：

Other Relatives of the R-tree Family and Other Tree Structures / 3.5.7：

Generic Tree Structures / 3.5.8：

Bitmap Indexing / 3.6：

Standard Bitmap Indexing / 3.6.1：

Multi-component Equality Encoded Bitmap Index / 3.6.2：

Range-Based Encoding / 3.6.3：

Multi-component Range-Based Encoding / 3.6.4：

Other Compression Techniques / Combination of Bitmaps and Trees / 3.6.5：

Arrays / 3.7：

Mixed Integer Problems for Finding Optimal Tree-Based Index Structures / 3.8：

Optimization Problem Parameters / 4.1：

Mapping into a Mixed Integer Problem / 4.3：

Problem Complexity / 4.4：

Model Evaluation / 4.5：

Aggregated Data in Tree-Based Index Structures / 4.6：

"Fit for Aggregation" Access Method / 5.1：

Materialization of Data / 5.3：

Modified Operations / 5.4：

Insert Operation / 5.4.1：

Delete Operation / 5.4.2：

Update Operation / 5.4.3：

Creating Index Structures, Bottom-Up Index Structures / 5.4.4：

Point Query Algorithm / 5.4.5：

Range Query Algorithm / 5.4.6：

Storage Cost / 5.5：

Height of Tree / 5.6：

Overlaps of Regions / 5.7：

Experiments / 5.8：

Cost Model / 5.8.1：

Physical Index Structure / 5.8.2：

Implementation / 5.8.3：

Generation of Test Data / 5.8.4：

Query Profile / 5.8.5：

Results of Experiments / 5.8.6：

Performance Models for Tree-Based Index Structures / 5.9：

Fit for Modeling / 6.1：

Performance Models for Access Leaf Nodes / 6.3：

GRID Model / 6.3.1：

SUM Model / 6.3.2：

Equivalence of GRID Model and SUM Model / 6.3.3：

FRACTAL Model / 6.3.4：

Equivalence between FRACTAL Model, SUM Model, and GRID Model / 6.3.5：

PISA Model / 6.4：

Computational Efficiency of SUM Model and PISA Model / 6.5：

Adapting PISA Model to Different Distributions / 6.6：

Uniformly Distributed Data / 6.6.1：

Skewed Data / 6.6.2：

Normally Distributed Data / 6.6.3：

PISA Model for Dependent Data / 6.7：

Extension of Models / 6.9：

Applications of Models / 6.10：

Savings of R*_a-tree Depending on the Query Box Size and Form / 6.10.1：

Savings of R*_a-tree Depending on the Number of Dimensions / 6.10.2：

Techniques for Comparing Index Structures / 6.11：

Experimental Parameters / 7.1：

Data Specific Parameters / 7.2.1：

Query Specific Parameters / 7.2.2：

System Specific Parameters / 7.2.3：

Disk Specific Parameters / 7.2.4：

Configuration / 7.2.5：

Index Structures and Time Estimators / 7.3：

Time Measures for Tree-Based Index Structures / 7.3.1：

Time Measures for Bitmap Indexing Techniques / 7.3.2：

Classification Trees / 7.4：

Applied Methods / 7.4.1：

Value Sets of Parameters / 7.4.2：

Results / 7.4.3：

Statistics in Two Dimensions / 7.5：

Sum Aggregation / 7.5.1：

Median Aggregation / 7.5.2：

Count Aggregation / 7.5.3：

Conclusion and Outlook / 7.5.4：

List of Symbols / A：

Approximation of PISA Model / B：

Bibliography

Index

Introduction / 1：

Goals / 1.1：

Outline / 1.2：

State of the Art of Data Warehouse Research / 2：

Traditional Transaction-Oriented Systems / 2.1：

Data Warehouses for Decision Support / 2.3：

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