Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2009

Abstract

This paper studies a new form of nearest neighbor queries in spatial databases, namely, mutual nearest neighbour (MNN) search. Given a set D of objects and a query object q, an MNN query returns from D, the set of objects that are among the k1 (≥ 1) nearest neighbors (NNs) of q; meanwhile, have q as one of their k2(≥ 1) NNs. Although MNN queries are useful in many applications involving decision making, data mining, and pattern recognition, it cannot be efficiently handled by existing spatial query processing approaches. In this paper, we present the first piece of work for tackling MNN queries efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree, etc.) on the dataset, employ the state-of-the-art database techniques including best-first based k nearest neighbor (kNN) retrieval and reverse kNN search with TPL pruning, and make use of the advantages of batch processing and reusing technique. An extensive empirical study, based on experiments performed using both real and synthetic datasets, has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.

Keywords

Query processing, Nearest neighbor, Spatial databases, Algorithm

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Data and Knowledge Engineering

Volume

68

Issue

8

First Page

705

Last Page

727

ISSN

0169-023X

Identifier

10.1016/j.datak.2009.04.004

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.datak.2009.04.004

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