Querying spatial patterns

Spatial data are common in many scientific and commercial domains such as geographical information systems and gene/protein expression profiles. Querying for distribution patterns on such data can discover underlying spatial relationships and suggest avenues for further scientific exploration. Supporting such pattern retrieval requires not only the formulation of an appropriate scoring function for defining relevant connected subregions, but also the design of new access methods that can scale to large databases. In this paper, we propose a solution to this problem of querying significant sub-regions on spatial data provided as raster images. We design a scoring scheme to measure the similarity of subregions. All the raster images are tiled and each alignment of the query and a database image produces a tile score matrix. We show that the problem of finding the best connected subregion from this matrix is NP-hard and develop a dynamic programming heuristic. With this heuristic, we develop two index-based scalable search strategies, TARS and SPARS, to query patterns in large data repositories. Experimental results on real image datasets show that TARS offers an 87% improvement for small queries, and SPARS a 52% improvement in runtime for large queries, as compared to linear search. Qualitative tests on real datasets achieve precision of more than 80%.