Evaluating pattern matching queries for spatial databases

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 pattern P . 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.