TokenJoin: Efficient Filtering for Set Similarity Join with Maximum Weighted Bipartite Matching

Set similarity join is an important problem with many applications in data discovery, cleaning and integration. To increase robustness, fuzzy set similarity join calculates the similarity of two sets based on maximum weighted bipartite matching instead of set overlap. This allows pairs of elements, represented as sets or strings, to also match approximately rather than exactly, e.g., based on Jaccard similarity or edit distance. However, this significantly increases the verification cost, making even more important the need for efficient and effective filtering techniques to reduce the number of candidate pairs. The current state-of-the-art algorithm relies on similarity computations between pairs of elements to filter candidates. In this paper, we propose token-based instead of element-based filtering, showing that it is significantly more lightweight, while offering similar or even better pruning effectiveness. Moreover, we address the top-k variant of the problem, alleviating the need for a user-specified similarity threshold. We also propose early termination to reduce the cost of verification. Our experimental results on six real-world datasets show that our approach always outperforms the state of the art, being an order of magnitude faster on average.