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Evaluating pattern matching queries for spatial databases

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Abstract

In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from D that match a user-defined spatial patternP. A pattern P is a graph whose vertices represent spatial objects, and edges denote distance relationships between them. The SPM query returns the instances that satisfy P. An example of P can be “a house within 10-min walk from a school, which is at least 2 km away from a hospital.” The SPM query can benefit users such as house buyers, urban planners, and archeologists. We prove that answering such queries is computationally intractable and propose two efficient algorithms for their evaluation. Moreover, we study efficient solutions to address two related problems of the SPM: (1) find top-k matches that are close to a query location and (2) return partial matches for a query pattern. Experiments and case studies on real datasets show that our proposed solutions are highly effective and efficient.

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Notes

  1. The user can input the lower/upper bounds of the intervals based on his experience and expertise. Alternatively, the system can be designed to give suggestions, based on, for instance, the previous users’ inputs or query results.

  2. In context without ambiguity, we simply call it a pattern.

  3. To avoid ambiguity, we use “node” to mean “IR-tree node,” and “vertex” to mean “vertex” of the spatial pattern in this paper.

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Acknowledgements

Reynold Cheng was supported by the Research Grants Council of Hong Kong (RGC Projects HKU 17229116, 106150091, and 17205115) and the University of Hong Kong (Projects 104004572, 102009508, and 104004129), and the Innovation and Technology Commission of Hong Kong (ITF project MRP/029/18). Nikos Mamoulis was supported by grant HKU 17253616 from Hong Kong RGC. Gao Cong is supported in part by a MOE Tier-2 grant MOE2016-T2-1-137 and a MOE Tier-1 grant RG31/17.

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Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Yixiang Fang & Reynold Cheng

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Yun Li

  3. Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Gao Cong

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  1. Yixiang Fang
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  2. Yun Li
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  3. Reynold Cheng
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  4. Nikos Mamoulis
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  5. Gao Cong
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Correspondence to Yixiang Fang.

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Fang, Y., Li, Y., Cheng, R. et al. Evaluating pattern matching queries for spatial databases. The VLDB Journal 28, 649–673 (2019). https://doi.org/10.1007/s00778-019-00550-3

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