INSPIRE: A Framework for Incremental Spatial Prefix Query Relaxation
Yuxin Zheng, Zhifeng Bao, Lidan Shou, Anthony K. H. Tung · IEEE Transactions on Knowledge and Data Engineering · 2015
Geo-textual data are generated in abundance. Recent studies focused on the processing of spatial keyword queries which retrieve objects that match certain keywords within a spatial region. To ensure effective retrieval, various extensions were done including the allowance of errors in keyword matching and auto completion using prefix matching. In this paper, we propose INSPIRE, a general framework, which adopts a unifying strategy for processing different variants of spatial keyword queries. We adopt the auto completion paradigm that generates an initial query as a prefix matching query. If there are few matching results, other variants are performed as a form of relaxation that reuses the processing done in the earlier phase. The types of relaxation allowed include spatial region expansion and exact/approximate prefix/substring matching. Moreover, since the auto completion paradigm allows appending characters after the initial query, we look at how query processing done for the initial query and relaxation can be reused in such instances. Compared to existing works which process variants of spatial keyword query as new queries over different indexes, our approach offers a more compelling way to efficient and effective spatial keyword search. Extensive experiments substantiate our claims.