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E-raamat: High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity Searches

  • Formaat: PDF+DRM
  • Sari: Lecture Notes in Computer Science 2341
  • Ilmumisaeg: 01-Aug-2003
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783540457701
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High-Dimensional Indexing: Transformational Approaches to High-Dimensional Range and Similarity SearchesSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Lecture Notes in Computer Science 2341
  • Ilmumisaeg: 01-Aug-2003
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783540457701
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A study of high-dimensional indexing describes two index structures, one for range queries and one for similarity queries.

In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed indexing methods.
Many new database applications, such as multimedia databases or stock price information systems, transform important features or properties of data objects into high-dimensional points. Searching for objects based on these features is thus a search of points in this feature space. To support efficient retrieval in such high-dimensional databases, indexes are required to prune the search space. Indexes for low-dimensional databases are well studied, whereas most of these application specific indexes are not scaleable with the number of dimensions, and they are not designed to support similarity searches and high-dimensional joins.

Muu info

Springer Book Archives
Introduction
1(8)
High-Dimensional Applications
1(3)
Motivations
4(3)
The Objectives and Contributions
7(1)
Organization of the Monograph
8(1)
High-Dimensional Indexing
9(28)
Introduction
9(2)
Hierarchical Multi-dimensional Indexes
11(6)
The R-tree
11(3)
Use of Larger Fanouts
14(1)
Use of Bounding Spheres
15(1)
The kd-tree
16(1)
Dimensionality Reduction
17(9)
Indexing Based on Important Attributes
18(1)
Dimensionality Reduction Based on Clustering
18(2)
Mapping from Higher to Lower Dimension
20(2)
Indexing Based on Single Attribute Values
22(4)
Filtering and Refining
26(3)
Multi-step Processing
26(1)
Quantization
27(2)
Indexing Based on Metric Distance
29(3)
Approximate Nearest Neighbor Search
32(1)
Summary
33(4)
Indexing the Edges -- A Simple and Yet Efficient Approach to High-Dimensional Range Search
37(28)
Introduction
37(1)
Basic Concept of iMinMax
38(8)
Sequential Scan
41(1)
Indexing Based on Max/Min
41(1)
Indexing Based on iMax
42(3)
Preliminary Empirical Study
45(1)
The iMinMax Method
46(1)
Indexing Based on iMinMax
47(2)
The iMinMax(θ)
49(3)
Processing of Range Queries
52(5)
iMinMax(θ) Search Algorithms
57(1)
Point Search Algorithm
57(1)
Range Search Algorithm
57(1)
Discussion on Update Algorithms
58(1)
The iMinMax(θi)
58(6)
Determining θi
59(3)
Refining θi
62(1)
Generating the Index Key
63(1)
Summary
64(1)
Performance Study of Window Queries
65(20)
Introduction
65(1)
Implementation
65(1)
Generation of Data Sets and Window Queries
66(1)
Experiment Setup
66(1)
Effect of the Number of Dimensions
67(2)
Effect of Data Size
69(1)
Effect of Skewed Data Distributions
70(6)
Effect of Buffer Space
76(1)
CPU Cost
77(1)
Effect of θi
78(2)
Effect of Quantization on Feature Vectors
80(3)
Summary
83(2)
Indexing the Relative Distance -- An Efficient Approach to KNN Search
85(24)
Introduction
85(1)
Background and Notations
86(1)
The iDistance
87(8)
The Big Picture
88(2)
The Data Structure
90(1)
KNN Search in iDistance
91(4)
Selection of Reference Points and Data Space Partitioning
95(7)
Space-Based Partitioning
96(3)
Data-Based Partitioning
99(3)
Exploiting iDistance in Similarity Joins
102(5)
Join Strategies
102(1)
Similarity Join Strategies Based on iDistance
103(4)
Summary
107(2)
Similarity Range and Approximate KNN Searches with iMinMax
109(14)
Introduction
109(1)
A Quick Review of iMinMax(θ)
109(1)
Approximate KNN Processing with iMinMax
110(5)
Quality of KNN Answers Using iMinMax
115(5)
Accuracy of KNN Search
118(1)
Bounding Box Vs. Bounding Sphere
118(1)
Effect of Search Radius
118(2)
Summary
120(3)
Performance Study of Similarity Queries
123(18)
Introduction
123(1)
Experiment Setup
123(1)
Effect of Search Radius on Query Accuracy
123(3)
Effect of Reference Points on Space-Based Partitioning Schemes
126(1)
Effect of Reference Points on Cluster-Based Partitioning Schemes
127(4)
CPU Cost
131(2)
Comparative Study of iDistance and iMinMax
133(1)
Comparative Study of iDistance and A-tree
134(2)
Comparative Study of the iDistance and M-tree
136(1)
iDistance -- A Good Candidate for Main Memory Indexing?
137(2)
Summary
139(2)
Conclusions
141(4)
Contributions
141(1)
Single-Dimensional Attribute Value Based Indexing
141(1)
Metric-Based Indexing
142(1)
Discussion on Future Work
143(2)
References 145
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