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Geo-Social Keyword Skyline Queries

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Database and Expert Systems Applications (DEXA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10438))

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Abstract

Location-Based Social Networking Services (LBSNSs) have been becoming increasingly popular. One of the applications provided by LBSNSs is a PoI search based on spatial distance, social relationships, and keywords. In this paper, we propose a novel query, Geo-Social Keyword Skyline Query (GSKSQ), which returns the skyline of a set of PoIs based on a query point, the social relationships of the query owner, and query keywords. Skyline is the set of data objects which are not dominated by others. We also propose an index structure, Social Keyword R-tree, which supports efficient GSKSQ processing. The results of our experiments on two real datasets Gowalla and Brightkite demonstrate the efficiency of our solution.

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Notes

  1. 1.

    https://snap.stanford.edu/data/loc-gowalla.html.

  2. 2.

    https://snap.stanford.edu/data/loc-brightkite.html.

References

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Author information

Authors and Affiliations

  1. University of Tokyo, Tokyo, Japan

    Naoya Taguchi

  2. Osaka University, Suita, Japan

    Daichi Amagata & Takahiro Hara

Authors
  1. Naoya Taguchi
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  2. Daichi Amagata
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  3. Takahiro Hara
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Corresponding author

Correspondence to Naoya Taguchi .

Editor information

Editors and Affiliations

  1. University of Lyon, Villeurbanne, France

    Djamal Benslimane

  2. University of Milan, Milan, Italy

    Ernesto Damiani

  3. University of Michigan, Dearborn, Michigan, USA

    William I. Grosky

  4. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  5. Wright State University, Dayton, Ohio, USA

    Amit Sheth

  6. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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© 2017 Springer International Publishing AG

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Taguchi, N., Amagata, D., Hara, T. (2017). Geo-Social Keyword Skyline Queries. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-64468-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64467-7

  • Online ISBN: 978-3-319-64468-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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