Similarity join is one of the hottest topics in the field of data management,and it has been widely applied in many fields.However,existing similarity join methods cannot meet the increasing demands in the real world.This paper define generalized bisimilarity join as a new similarity join to expend the applications of the similarity join research by introducing the satisfaction operator on various data types with individual thresholds.Two efficient methods,SJS (sub-join set) and MFV (mapping-filtering-verification),are proposed to solve this problem.A large amount of experiments conducted on both real-world and synthetic datasets demonstrate the correctness and the effectiveness of the proposed methods.%相似连接是数据管理领域的一个热门话题,已在社会生产生活中得到广泛应用.然而,现有的相似连接方法并不能满足真实世界不断增长的客观需求.通过引入定义在多种数据类型上的满足操作符和每条数据的独立阈值,定义了一种相似连接——泛化双向相似连接.这种连接扩展了相似连接的应用范围.同时,还提出了两种高效的解决泛化双向相似连接问题的方法:子连接集算法和映射-过滤-验证算法.通过真实与合成数据集上的大量实验,得出了所提方法的正确性和有效性.
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