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Diversified Top-k Spatial Pattern Matching

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Spatial Data and Intelligence (SpatialDI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13614))

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Abstract

Spatial Pattern Matching (SPM) is used in various location-based services. SPM usually returns many matches which maybe overlap due to continuous geographic space, while users tend towards diversified top-k matches. Furthermore, existing algorithms to find all matches is low efficient. To solve the above problems, this paper proposes an efficient approximate algorithm DivMatch to attain diversified top-k matches. Firstly, we introduce two metrics \( N_{spatial}\) and \(D_{spatial} \) to measure the nearness between a match and a query location, and the diversity between two matches. Based on the two metrics, we define an objective function F to select diversified top-k matches. Then, we present an approximate algorithm to efficiently find k matches which maximize the objective function based on level-by-level searching strategy. Experiment results on four real datasets show that DivMatch is more effective and efficient than the algorithm IncMatch.

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Acknowledgements

This work is supported by the Open Project Program of Yunnan Key Laboratory of Intelligent Systems and Computing (ISC22Z02), the Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province (202205AC160033), the National Natural Science Foundation of China (61966036), Yunnan Provincial Major Science and Technology Special Plan Projects (202202AD080003), and Yunnan Fundamental Research Projects (202201AS070015).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Jiahua Xie, Hongmei Chen & Lizhen Wang

  2. Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China

    Hongmei Chen & Lizhen Wang

Authors
  1. Jiahua Xie
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  2. Hongmei Chen
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  3. Lizhen Wang
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Corresponding author

Correspondence to Hongmei Chen .

Editor information

Editors and Affiliations

  1. Peking University, Wuhan, China

    Huayi Wu

  2. Peking University, Beijing, China

    Yu Liu

  3. Harbin Institute of Technology, Harbin, China

    Jianzhong Li

  4. Department of Computer Science and Information, Renmin University of China, Beijing, China

    Xiaofeng Meng

  5. China University of Geosciences (Wuhan), Wuhan, China

    Qingfeng Guan

  6. Southern University of Science and Technology, Shenzhen, China

    Xuan Song

  7. Jiangxi University of Finance and Economics, Nanchang, China

    Guoqiong Liao

  8. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Guoliang Li

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Xie, J., Chen, H., Wang, L. (2022). Diversified Top-k Spatial Pattern Matching. In: Wu, H., et al. Spatial Data and Intelligence. SpatialDI 2022. Lecture Notes in Computer Science, vol 13614. Springer, Cham. https://doi.org/10.1007/978-3-031-24521-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-24521-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24520-6

  • Online ISBN: 978-3-031-24521-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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