Reverse kNN search in arbitrary dimensionality

  • Authors:
  • Yufei Tao;Dimitris Papadias;Xiang Lian

  • Affiliations:
  • Department of Computer Science, City University of Hong Kong, Hong Kong;Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

  • Venue:
  • VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
  • Year:
  • 2004

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Abstract

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.