A partition-based method for string similarity joins with edit-distance constraints

As an essential operation in data cleaning, the similarity join has attracted considerable attention from the database community. In this article, we study string similarity joins with edit-distance constraints, which find similar string pairs from two large sets of strings whose edit distance is within a given threshold. Existing algorithms are efficient either for short strings or for long strings, and there is no algorithm that can efficiently and adaptively support both short strings and long strings. To address this problem, we propose a new filter, called the segment filter. We partition a string into a set of segments and use the segments as a filter to find similar string pairs. We first create inverted indices for the segments. Then for each string, we select some of its substrings, identify the selected substrings from the inverted indices, and take strings on the inverted lists of the found substrings as candidates of this string. Finally, we verify the candidates to generate the final answer. We devise efficient techniques to select substrings and prove that our method can minimize the number of selected substrings. We develop novel pruning techniques to efficiently verify the candidates. We also extend our techniques to support normalized edit distance. Experimental results show that our algorithms are efficient for both short strings and long strings, and outperform state-of-the-art methods on real-world datasets.