A Trie Based Set Similarity Query Algorithm

Set similarity query is a primitive for many applications, such as data integration, cleaning, and gene sequence alignment. Most of the existing algorithms are inverted index based, they usually filter unqualified sets one by do not have sufficient support duplicated sets, thus leading to low efficiency. To solve this problem, paper designs T-starTrie, an efficient trie based set query, which can naturally group with same prefix into node, all corresponding node at time, thereby significantly improving candidates generation In paper, we find that problem be transformed matching nodes first-layer (FMNodes) detecting on T-starTrie. Therefore, FLMNode detection algorithm designed. Based this, algorithm, TT-SSQ, implemented developing variety filtering techniques. Experimental results show TT-SSQ up 3.10x faster than algorithms.