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An efficient algorithm for spatio-textual location matching

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Abstract

Geospatial location matching plays a significant role in spatial databases. In this paper, we propose and study a novel parallel spatio-textual location matching (STLM) query. Given two sets P and Q of spatial locations with textual attributes, a spatio-textual matching threshold \(\theta \), the STLM query finds all location pairs whose spatio-textual similarity exceeds \(\theta \). We believe that the STLM query is useful in many applications such as important location/hot region detection, duplicate spatio-textual data cleaning, and location based services in general. The STLM query is challenging due to three reasons: (1) how to evaluate the spatio-textual similarity between two locations practically, (2) how to prune the search space effectively in both spatial and textual domains, and (3) how to process the STLM query in parallel because of its high computation complexity. To overcome the challenges, we develop a novel direct matching (DM) search algorithm. A linear combination method is adopted to combine the spatial proximity and textual similarity together. To further improve the query efficiency, we develop a grid-based expansion scheduling scheme based on a purposeful grid index structure. We conduct extensive experiments on real and synthetic spatio-textual data sets to verify the performance of the developed algorithms.

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  1. https://www.google.com/maps

  2. https://twitter.com

  3. https://www.facebook.com

  4. https://foursquare.com

  5. https://foursquare.com

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Acknowledgements

This study is supported by the Program of New Century Excellent Talents in Fujian Province University.

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Authors and Affiliations

  1. Department of Automation, Xiamen University, College of Information and Smart Electromechanical Engineering, Xiamen Huaxia University, and Information Communication Technology and Smart Education of Fujian Engineering Research Center, Fujian, China

    Ning Wang

  2. Department of Automation, Xiamen University, Fujian, China

    Jianping Zeng

  3. College of Information and Smart Electromechanical Engineering, Xiamen Huaxia University, and Information Communication Technology and Smart Education of Fujian Engineering Research Center, Fujian, China

    Mingming Chen

  4. School of Computer and Information Engineering, Xiamen University of Technology, Fujian, China

    Shunzhi Zhu

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  1. Ning Wang
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  2. Jianping Zeng
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  4. Shunzhi Zhu
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Correspondence to Jianping Zeng.

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Wang, N., Zeng, J., Chen, M. et al. An efficient algorithm for spatio-textual location matching. Distrib Parallel Databases 38, 649–666 (2020). https://doi.org/10.1007/s10619-020-07289-9

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