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Towards Spatially- and Category-Wise k-Diverse Nearest Neighbors Queries

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Advances in Spatial and Temporal Databases (SSTD 2017)

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Abstract

k-nearest neighbor (k-NN) queries are well-known and widely used in a plethora of applications. In the original definition of k-NN queries there is no concern regarding diversity of the answer set, even though in some scenarios it may be interesting. For instance, travelers may be looking for touristic sites that are not too far from where they are but that would help them seeing different parts of the city. Likewise, if one is looking for restaurants close by, it may be more interesting to return restaurants of different categories or ethnicities which are nonetheless relatively close. The interesting novel aspect of this type of query is that there are competing criteria to be optimized. We propose two approaches that leverage the notion of linear skyline queries in order to find spatially- and category-wise diverse k-NNs w.r.t. a given query point and which return all optimal solutions for any linear combination of the weights a user could give to the two competing criteria. Our experiments, varying a number of parameters, show that our approaches are several orders of magnitude faster than a straightforward approach.

This research has been partially supported by NSERC, Canada and CNPq’s Science Without Borders program, Brazil.

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Notes

  1. 1.

    For simplicity we assume that CS is the set of |CS|-NN wrt the query point.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Canada

    Camila F. Costa & Mario A. Nascimento

Authors
  1. Camila F. Costa
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  2. Mario A. Nascimento
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Corresponding author

Correspondence to Camila F. Costa .

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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F. Costa, C., Nascimento, M.A. (2017). Towards Spatially- and Category-Wise k-Diverse Nearest Neighbors Queries. 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_9

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  • DOI: https://doi.org/10.1007/978-3-319-64367-0_9

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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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