A string similarity join finds similar pairs between two collections of strings. It is an essential operation in many applications, such as data integration and cleaning, and has attracted significant attention recently. In this paper, we study string similarity joins with edit-distance constraints. Existing methods usually employ a filter-and-refine framework and have the following disadvantages: (1) They are inefficient for the data sets with short strings (the average string length is no larger than 30); (2) They involve large indexes; (3) They are expensive to support dynamic update of data sets. To address these problems, we propose a novel framework called trie-join, which can generate results efficiently with small indexes. We use a trie structure to index the strings and utilize the trie structure to efficiently find the similar string pairs based on subtrie pruning. We devise efficient trie-join algorithms and pruning techniques to achieve high performance. Our method can be easily extended to support dynamic update of data sets efficiently. Experimental results show that our algorithms outperform state-of-the-art methods by an order of magnitude on three real data sets with short strings.
Wang et al. REF proposed a trie-based framework to support similarity joins.
Trie-Join: Efficient Trie-based String Similarity Joins with Edit-Distance Constraints
PVLDB
PVLDB
Computer Science