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Augmented keyword search on spatial entity databases

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Abstract

In this paper, we propose a new type of query that augments the spatial keyword search with an additional boolean expression constraint. The query is issued against a corpus of structured or semi-structured spatial entities and is very useful in applications like mobile search and targeted location-aware advertising. We devise three types of indexing and filtering strategies. First, we utilize the hybrid \(\hbox {IR}^2\)-tree and propose a novel hashing scheme for efficient pruning. Second, we propose an inverted index-based solution, named BE-Inv, that is more cache concious and exhibits great pruning power for boolean expression matching. Our third method, named SKB-Inv, adopts a novel two-level partitioning scheme to organize the spatial entities into inverted lists and effectively facilitate the pruning in the spatial, textual, and boolean expression dimensions. In addition, we propose an adaptive query processing strategy that takes into account the selectivity of query keywords and predicates for early termination. We conduct our experiments using two real datasets with 3.5 million Foursquare venues and 50 million Twitter geo-profiles. The results show that the methods based on inverted index are superior to the hybrid \(\hbox {IR}^2\)-tree; and SKB-Inv achieves the best performance.

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Notes

  1. https://www.statista.com/chart/1520/number-of-monthly-active-twitter-users/

  2. http://www.statista.com/statistics/277958/number-of-mobile-active-facebook-users-worldwide/

  3. The location information can be naturally derived from the GPS of smartphones.

  4. https://www.yelp.com/

  5. https://www.gotinder.com/

  6. http://appcrawlr.com/app/search?q=sort+by+distance&device=ios

  7. With the prevalence of large-memory machines, there is a trend of designing memory-resident indexes for efficient information retrieval. For instance, Twitter’s EarlyBird system [2] is designed to be in-memory to support real-time keyword search. Our paper follows the trend and assumes that the index can be accommodated in memory.

  8. https://local.google.com/

  9. https://local.yahoo.com/

  10. https://developer.foursquare.com/

  11. http://www.gregsadetsky.com/aol-data/.

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Authors and Affiliations

  1. Center for Future Media and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Dongxiang Zhang, Jie Shao & Heng Tao Shen

  2. School of Information Systems, Singapore Management University, Singapore, Singapore

    Yuchen Li

  3. School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia

    Xin Cao

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  1. Dongxiang Zhang
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  2. Yuchen Li
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  3. Xin Cao
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  4. Jie Shao
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Correspondence to Heng Tao Shen.

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Zhang, D., Li, Y., Cao, X. et al. Augmented keyword search on spatial entity databases. The VLDB Journal 27, 225–244 (2018). https://doi.org/10.1007/s00778-018-0497-6

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