ESPM: Efficient Spatial Pattern Matching (Extended Abstract)

Abstract

A huge volume of spatio-textual objects generated from location-based services enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture users' intention. It has been demonstrated to be useful but computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. Our experimental results on real large datasets show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost.