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Exploiting location-aware social networks for efficient spatial query processing

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Abstract

In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users’ check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people’s movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users’ check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks.

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Notes

  1. http://snap.stanford.edu/data/loc-gowalla.html.

  2. http://snap.stanford.edu/data/loc-gowalla.html.

  3. http://www.cs.utah.edu/lifeifei/SpatialDataset.htm.

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Acknowledgments

This research has been funded in part by the National Science Foundation grant CNS-0917137 and the Faculty Scholarship award from Valdosta State University.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849, USA

    Liang Tang & Wei-Shinn Ku

  2. Department of Mathematics and Computer Science, Valdosta State University, Valdosta, GA, 31698, USA

    Haiquan Chen

  3. Department of Computer Science and Information Engineering, National Central University, Taoyuan, 320, Taiwan

    Min-Te Sun

Authors
  1. Liang Tang
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  2. Haiquan Chen
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  3. Wei-Shinn Ku
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  4. Min-Te Sun
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Correspondence to Wei-Shinn Ku.

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Tang, L., Chen, H., Ku, WS. et al. Exploiting location-aware social networks for efficient spatial query processing. Geoinformatica 21, 33–55 (2017). https://doi.org/10.1007/s10707-016-0271-0

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