article

iDistance: An adaptive B⁺-tree based indexing method for nearest neighbor search

Authors:

H. V. Jagadish,

Rui ZhangAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 30, Issue 2

Pages 364 - 397

https://doi.org/10.1145/1071610.1071612

Published: 01 June 2005 Publication History

Abstract

In this article, we present an efficient B⁺-tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a B⁺-tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution.We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization.

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Index Terms

iDistance: An adaptive B⁺-tree based indexing method for nearest neighbor search
1. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 30, Issue 2

June 2005

328 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/1071610

Issue’s Table of Contents

Copyright © 2005 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2005

Published in TODS Volume 30, Issue 2

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