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Efficient processing of moving collective spatial keyword queries

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Abstract

As a major type of continuous spatial queries, the moving spatial keyword queries have been studied extensively. Most existing studies focus on retrieving single objects, each of which is close to the query object and relevant to the query keywords. Nevertheless, a single object may not satisfy all the needs of a user, e.g., a user who is driving may want to withdraw money, wash her car, and buy some medicine, which could only be satisfied by multiple objects. We thereby formulate a new type of queries named the moving collective spatial keyword query (MCSKQ). This type of queries continuously reports a set of objects that collectively cover the query keywords as the query moves. Meanwhile, the returned objects must also be close to the query object and close to each other. Computing the exact result set is an NP-hard problem. To reduce the query processing costs, we propose algorithms, based on safe region techniques, to maintain the exact result set while the query object is moving. We further propose two approximate algorithms to obtain even higher query efficiency with precision bounds. All the proposed algorithms are also applicable to MCSKQ with weighted objects and MCSKQ in the domain of road networks. We verify the effectiveness and efficiency of the proposed algorithms both theoretically and empirically, and the results confirm the superiority of the proposed algorithms over the baseline algorithms.

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Notes

  1. https://www.zelda.com/.

  2. https://elderscrolls.bethesda.net/.

  3. Given a query q and two objects, \(o_i\) and \(o_j\), the dominant region of \(o_i\) to \(o_j\) is a region such that if q is in the region, \(o_i\) is a better answer than \(o_j\).

  4. http://www.cs.utah.edu/~lifeifei/.

  5. http://www.chorochronos.org/.

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Acknowledgements

This work is supported by the National Key R&D Program of China (2018YFB1003404), the National Natural Science Foundation of China (61872070, U1811261), the Fundamental Research Funds for the Central Universities (N171605001) and Liao Ning Revitalization Talents Program (XLYC1807158).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, China

    Hongfei Xu, Yu Gu & Ge Yu

  2. Twitter, Inc., San Francisco, CA, USA

    Yu Sun

  3. The Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Jianzhong Qi & Rui Zhang

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  1. Hongfei Xu
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  2. Yu Gu
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  3. Yu Sun
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  4. Jianzhong Qi
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  5. Ge Yu
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Correspondence to Yu Gu.

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Xu, H., Gu, Y., Sun, Y. et al. Efficient processing of moving collective spatial keyword queries. The VLDB Journal 29, 841–865 (2020). https://doi.org/10.1007/s00778-019-00583-8

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