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Maximizing bichromatic reverse nearest neighbor for L p -norm in two- and three-dimensional spaces

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Abstract

Bichromatic reverse nearest neighbor (BRNN) has been extensively studied in spatial database literature. In this paper, we study a related problem called MaxBRNN: find an optimal region that maximizes the size of BRNNs for L p -norm in two- and three- dimensional spaces. Such a problem has many real-life applications, including the problem of finding a new server point that attracts as many customers as possible by proximity. A straightforward approach is to determine the BRNNs for all possible points that are not feasible since there are a large (or infinite) number of possible points. To the best of our knowledge, there are no existing algorithms which solve MaxBRNN for any L p -norm space of two- and three-dimensionality. Based on some interesting properties of the problem, we come up with an efficient algorithm called MaxOverlap for to solve this problem. Extensive experiments are conducted to show that our algorithm is efficient.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Hong Kong, People’s Republic of China

    Raymond Chi-Wing Wong & Lian Liu

  2. University of Waterloo, Waterloo, Canada

    M. Tamer Özsu

  3. The Chinese University of Hong Kong, Hong Kong, People’s Republic of China

    Ada Wai-Chee Fu

  4. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  5. Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Yubao Liu

Authors
  1. Raymond Chi-Wing Wong
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  2. M. Tamer Özsu
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  3. Ada Wai-Chee Fu
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  4. Philip S. Yu
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  5. Lian Liu
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  6. Yubao Liu
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Corresponding author

Correspondence to Raymond Chi-Wing Wong.

Additional information

This is an extended version of the paper that appeared in the International Conference on Very Large Databases, 2009.

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Wong, R.CW., Özsu, M.T., Fu, A.WC. et al. Maximizing bichromatic reverse nearest neighbor for L p -norm in two- and three-dimensional spaces. The VLDB Journal 20, 893–919 (2011). https://doi.org/10.1007/s00778-011-0230-1

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  • DOI: https://doi.org/10.1007/s00778-011-0230-1

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