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Aggregate k Nearest Neighbor Queries in Metric Spaces

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Web and Big Data (APWeb-WAIM 2018)

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

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Abstract

Aggregate k nearest neighbor (AkNN) queries are useful in many areas, such as multimedia retrieval and resource allocation, to name but a few. Most of existing works on AkNN query only focus on Euclidean space or specific metric space, which employ properties of particular data to accelerate the query. However, due to the complex data types involved and the needs for flexible similarity criteria seen in real applications, properties of particular data cannot be used for general case. Hence, in this paper, we investigate AkNN search in metric spaces, termed as metric AkNN (MAkNN) search, as metric spaces can support any type of data and flexible similarity criteria as long as satisfying triangle inequality. To efficiently answer MAkNN queries, we develop several pruning techniques and corresponding algorithms based on SPB-tree. Extensive experiments using three real data sets verify the efficiency of our MAkNN algorithms.

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Notes

  1. 1.

    Words is available at http://icon.shef.ac.uk/Moby/.

  2. 2.

    Color is available at http://www.sisap.org/Metric_Space_Library.html.

  3. 3.

    DNA is available at http://www.ncbi.nlm.nih.gov/genome.

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Acknowledgments

This work was supported in part by the 973 Program of China under Grant No. 2015CB352502, the NSFC under Grant No. 61522208, the NSFC-Zhejiang Joint Fund under Grant No. U1609217, and the ZJU-Hikvision Joint Project.

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, China

    Xin Ding, Yuanliang Zhang, Keyu Yang & Yunjun Gao

  2. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Lu Chen

Authors
  1. Xin Ding
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  2. Yuanliang Zhang
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  3. Lu Chen
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  4. Keyu Yang
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  5. Yunjun Gao
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Corresponding author

Correspondence to Yunjun Gao .

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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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Ding, X., Zhang, Y., Chen, L., Yang, K., Gao, Y. (2018). Aggregate k Nearest Neighbor Queries in Metric Spaces. 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_24

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  • DOI: https://doi.org/10.1007/978-3-319-96893-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96892-6

  • Online ISBN: 978-3-319-96893-3

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