With the increasing amount of data and the need to integrate data from multiple data sources, one of the challenging issues is to identify near-duplicate records efficiently. In this article, we focus on efficient algorithms to find a pair of records such that their similarities are no less than a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the token ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. We have also studied the implementation of our proposed algorithm in stand-alone and RDBMS-based settings. Experimental results show our proposed algorithms can outperform previous algorithms on several real datasets.

There has been considerable interest in similarity join in the research community recently. Similarity join is a fundamental operation in many application areas, such as data integration and cleaning, bioinformatics, and pattern recognition. We focus on efficient algorithms for similarity join with edit distance constraints. Existing approaches are mainly based on converting the edit distance constraint to a weaker constraint on the number of matching q -grams between pair of strings. In this paper, we propose the novel perspective of investigating mismatching q -grams. Technically, we derive two new edit distance lower bounds by analyzing the locations and contents of mismatching q -grams. A new algorithm, Ed-Join, is proposed that exploits the new mismatch-based filtering methods; it achieves substantial reduction of the candidate sizes and hence saves computation time. We demonstrate experimentally that the new algorithm outperforms alternative methods on large-scale real datasets under a wide range of parameter settings.

Similarity join is a useful primitive operation underlying many applications, such as near duplicate Web page detection, data integration, and pattern recognition. Traditional similarity joins require a user to specify a similarity threshold. In this paper, we study a variant of the similarity join, termed top-k set similarity join. It returns the top-k pairs of records ranked by their similarities, thus eliminating the guess work users have to perform when the similarity threshold is unknown before hand. An algorithm, topk-join, is proposed to answer top-k similarity join efficiently. It is based on the prefix filtering principle and employs tight upper bounding of similarity values of unseen pairs. Experimental results demonstrate the efficiency of the proposed algorithm on large-scale real datasets.