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Clustering Enhanced Error-tolerant Top-k Spatio-textual Search

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Abstract

There are a large amount of Location-Based Services widely available on a variety of portable electronic devices. It is critical for them to efficiently support top-kquery considering both spatial and textual relevance. Considering both the errors in user input and the spatial databases, it is necessary to support error-tolerant spatio-textual search for end-users. Previous researches mainly focused on set-based textual relevance, which makes it difficult for them to find reasonable results when the input tokens are not exactly matched with those from the records in spatial database. We design a novel framework to support top-kspatio-textual search with fuzzy token matching. A hierarchical index is proposed to capture signatures of both spatial and textual relevance. Based on it, we devise two algorithms to preferentially access the nodes with more similar objects while those with dissimilar ones can be pruned. We further propose a clustering based approach to construct the index by leveraging textual information. We conduct extensive experiments on real world POI datasets, and the results show that our framework outperforms state-of-the-art methods by a significant margin.

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  1. http://www.openstreetmap.org/

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Acknowledgments

This work was supported by National Key R&D Program of China(2020AAA0109603), State Key Laboratory of Computer Architecture (ICT,CAS) under Grant No. CARCHA202008.

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Authors and Affiliations

  1. BNRist,DCST, RIIT, Tsinghua University, Beijing, China

    Yong Zhang, Yu Chen, Junye Yang & Chunxiao Xing

  2. Computer Science Department, University of California, Los Angeles, CA, USA

    Jin Wang

  3. Institute for Data Science and Engineering, East China Normal University, Shanghai, China

    Huiqi Hu

  4. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Xiaofang Zhou

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  1. Yong Zhang
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  2. Yu Chen
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Zhang, Y., Chen, Y., Yang, J. et al. Clustering Enhanced Error-tolerant Top-k Spatio-textual Search. World Wide Web 24, 1185–1214 (2021). https://doi.org/10.1007/s11280-021-00883-6

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