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VLDB
2004
ACM

Reverse kNN Search in Arbitrary Dimensionality

9 years 7 months ago
Reverse kNN Search in Arbitrary Dimensionality
Given a point q, a reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k nearest neighbors. Existing methods for processing such queries have at least one of the following deficiencies: (i) they do not support arbitrary values of k (ii) they cannot deal efficiently with database updates, (iii) they are applicable only to 2D data (but not to higher dimensionality), and (iv) they retrieve only approximate results. Motivated by these shortcomings, we develop algorithms for exact processing of RkNN with arbitrary values of k on dynamic multidimensional datasets. Our methods utilize a conventional data-partitioning index on the dataset and do not require any pre-computation. In addition to their flexibility, we experimentally verify that the proposed algorithms outperform the existing ones even in their restricted focus.
Yufei Tao, Dimitris Papadias, Xiang Lian
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where VLDB
Authors Yufei Tao, Dimitris Papadias, Xiang Lian
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