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Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data

APWeb-WAIM 2018: Web and Big Data pp 368–376Cite as

Answering Range-Based Reverse kNN Queries

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10988))

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. Despite significant progress on this problem, there is a research gap in finding RkNNs not just for an object, but for a given range, which is a natural extension of the problem. Motivated by this, we develop algorithms for exact processing of range-based RkNN with arbitrary values of k on dynamic datasets, which retrieve all the data points that have any position in the given query range R as one of their k nearest neighbors. The experimental results demonstrate the efficiency and the accuracy of our proposed optimizations and algorithms.

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Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai, China

    Zhefan Zhong, Xin Lin, Liang He & Yan Yang

  2. Department of Computer Science and Technology, East China Normal University, Shanghai, 200241, China

    Zhefan Zhong, Xin Lin, Liang He & Yan Yang

Authors
  1. Zhefan Zhong
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  2. Xin Lin
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  3. Liang He
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  4. Yan Yang
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Corresponding author

Correspondence to Xin Lin .

Editor information

Editors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Yi Cai

  2. Nagoya University, Nagoya, Japan

    Yoshiharu Ishikawa

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

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© 2018 Springer International Publishing AG, part of Springer Nature

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Cite this paper

Zhong, Z., Lin, X., He, L., Yang, Y. (2018). Answering Range-Based Reverse kNN Queries. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_28

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  • DOI: https://doi.org/10.1007/978-3-319-96893-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96892-6

  • Online ISBN: 978-3-319-96893-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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