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In this paper, we study the problem of string similarity search to retrieve in a database all strings similar to a query string within a given threshold. To measure the similarity between strings, we use edit distance. Many algorithms have been proposed under a filtering-and-verification framework to solve the problem. To reduce the overhead of edit distance verification, it is crucial to efficiently generate a small number of candidates in the filtering phase. Recently, an index structure named HSTree has been proposed for efficiently generating candidate strings. To generate candidates, they select and utilize HSTree nodes at a specific level calculated from a given threshold. In this paper, we observe that there are many alternative ways to select HSTree nodes, and propose a novel technique that selects HSTree nodes in an optimized way based on the observation. We also propose a modified HSTree, named a threaded HSTree, which connects inverted lists of an HSTree node to inverted lists of its child nodes. With a threaded HSTree, we can reduce the overhead of index lookups in HSTree nodes while selecting optimal tree nodes. Experimental results show that the proposed technique significantly outperforms the existing technique using the HSTree.

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