Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2273

Reverse kNN Search in Arbitrary Dimensionality

Authors Tao, Yufei
Papadias, Dimitris
Lian, Xiang
Issue Date 2004
Source Proceedings of the Very Large Data Bases Conference (VLDB), Toronto, pp. 744-755, Aug. 30 - Sept. 3, 2004
Summary 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.
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Rights The original publication is available at http://www.springerlink.com/
Language English
Format Conference paper
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