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High-Dimensional Indexing

Transformational Approaches to High-Dimensional Range and Similarity Searches

  • Cui Yu

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2341)

About this book

Introduction

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.

Keywords

B-Trees Data Mining Database Querying High-Dimensional Indexes High-Dimensional Indexing Indexing Indexing Methods Information Retrieval KNN Queries Multimedia Performance Range Queries Searching Similarity Searches Window Queries

Editors and affiliations

  • Cui Yu
    • 1
    • 2
  1. 1.Department of Computer Science West Long BranchMonmouth UniversityUSA
  2. 2.Department of Computer ScienceNational University of SingaporeSingaporeSingapore

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-45770-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-44199-1
  • Online ISBN 978-3-540-45770-1
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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