PASS-JOIN：一种基于划分的相似连接方法
PASS-JOIN: A Partition-based Method for Similarity Joins
摘要：
相似连接作为数据清洗中的一个重要操作，受到了数据库界的广泛关注。在本文中，我们研究了字符串相似连接与编辑距离的限制，找到相似的字符串对从两个大的字符串集的编辑距离是在一个给定的阈值。现有的算法对短字符串或长字符串都是有效的，并且没有算法可以有效地和自适应地支持短字符串和长字符串。为了解决这个问题，我们提出了一个基于分区的方法称为Pass-Join。Pass-Join将字符串划分为一组段，并为这些段创建倒排索引。然后，对于每个字符串，Pass-Join选择它的一些子字符串，并使用所选择的子字符串来使用倒排索引找到候选对。我们设计了有效的技术来选择子串，并证明我们的方法可以最小化选定的子串的数量。我们开发了新的修剪技术，有效地验证候选对。实验结果表明，我们的算法是有效的短字符串和长字符串，并优于国家的最先进的方法在真实数据集上。
英文摘要：
As an essential operation in data cleaning, the similarity join has attracted considerable attention from the database community. In this paper, 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 partition-based method called Pass-Join. Pass-Join partitions a string into a set of segments and creates inverted indices for the segments. Then for each string, Pass-Join selects some of its substrings and uses the selected substrings to find candidate pairs using the inverted indices. We devise efficient techniques to select the substrings and prove that our method can minimize the number of selected substrings. We develop novel pruning techniques to efficiently verify the candidate pairs. Experimental results show that our algorithms are efficient for both short strings and long strings, and outperform state-of-the-art methods on real datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
