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

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

by Cui Yu
 

In this monograph, we study the problem of h-d 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

Overview

In this monograph, we study the problem of h-d 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 hd databases, indexes are required to prime 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 hd joins.

Product Details

ISBN-13:
9783540441991
Publisher:
Springer Berlin Heidelberg
Publication date:
11/13/2002
Series:
Lecture Notes in Computer Science Series , #2341
Edition description:
2002
Pages:
156
Product dimensions:
6.10(w) x 9.17(h) x (d)

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