Approximate string search in large spatial database

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Abstract

Applications are featured with both text and location information, which leads to a search like: spatial approximate string search (SAS). Mainly four issues are identified in the general area of SAS. They are: (i) Spatial approximate string search in Euclidean space (Esas); (ii) Spatial approximate string search on road networks (RSAS); (iii) Selectivity Estimation for Esas Range Queries; (iv) Multi-Approximate-Keyword Routing query on road networks. For efficiently answering spatial approximate string queries in Euclidean space, SAS propose a novel index structure, IR2-tree, which is based on the R-tree augmented with the min-wise signature and the linear hashing technique. Extensive experiments on large real data sets demonstrate the efficiency and effectiveness of the proposed approach.

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Tamilselvi, M., & Renuga, R. (2015). Approximate string search in large spatial database. In Procedia Computer Science (Vol. 47, pp. 92–100). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.03.187

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