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Collective-k Optimal Location Selection

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Advances in Spatial and Temporal Databases (SSTD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10411))

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Abstract

We study a novel location optimization problem, the Collective-k Optimal Location Selection (CkOLS) problem. This problem finds k regions such that setting up k service sites, one in each region, collectively attracts the maximum number of customers by proximity. The problem generalizes the traditional influence maximizing location selection problem from searching for one optimal region to k regions. This increases the complexity of the problem. We prove that the CkOLS problem is NP-hard, and propose both precise and approximate algorithms to solve this problem. The precise algorithm uses a brute-force search with a pruning strategy. The approximate algorithm adopts a clustering-based approach, which restricts the combinational search space within representative regions of clusters, and has a bounded approximation ratio. Extensive experiments show that the approximate algorithm is effective and efficient, and its output results are close to optimal.

This work is partially done when Fangshu is visiting the University of Melbourne.

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Notes

  1. 1.

    http://www.rtreeportal.org/spatial.html.

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Acknowledgements

This work was partly supported by The University of Melbourne Early Career Researcher Grant (project number 603049), National Science and Technology Supporting plan (2015BAH45F00), the public key plan of Zhejiang Province (2014C23005), the cultural relic protection science and technology project of Zhejiang Province.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, People’s Republic of China

    Fangshu Chen, Huaizhong Lin, Pengfei Li & Yunjun Gao

  2. School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Jianzhong Qi

Authors
  1. Fangshu Chen
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  2. Huaizhong Lin
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  3. Jianzhong Qi
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  4. Pengfei Li
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  5. Yunjun Gao
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Corresponding author

Correspondence to Huaizhong Lin .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Heidelberg University, Heidelberg, Germany

    Michael Gertz

  2. George Mason University, Fairfax, Virginia, USA

    Matthias Renz

  3. University of Queensland, Brisbane, Queensland, Australia

    Xiaofang Zhou

  4. ESRI, University of Minnesota, Minneapolis, Minnesota, USA

    Erik Hoel

  5. Auburn University, Auburn, Alabama, USA

    Wei-Shinn Ku

  6. Free University of Berlin, Dahlem, Berlin, Germany

    Agnes Voisard

  7. Microsoft, Redmond, Washington, USA

    Chengyang Zhang

  8. California State University, Sacramento, California, USA

    Haiquan Chen

  9. LinkedIn, Sunnyvale, California, USA

    Liang Tang

  10. University of North Texas, Denton, Texas, USA

    Yan Huang

  11. Virginia Tech, Falls Church, Virginia, USA

    Chang-Tien Lu

  12. Oracle, Redwood Shores, California, USA

    Siva Ravada

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Chen, F., Lin, H., Qi, J., Li, P., Gao, Y. (2017). Collective-k Optimal Location Selection. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_18

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  • DOI: https://doi.org/10.1007/978-3-319-64367-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64366-3

  • Online ISBN: 978-3-319-64367-0

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