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• W2125980212 abstract "String similarity join that finds similar string pairs between two string sets is an essential operation in many applications, and has attracted significant attention recently in the database community. A significant challenge in similarity join is to implement an effective fuzzy match operation to find all similar string pairs which may not match exactly. In this paper, we propose a new similarity metrics, called “fuzzy token matching based similarity”, which extends token-based similarity functions (e.g., Jaccard similarity and Cosine similarity) by allowing fuzzy match between two tokens. We study the problem of similarity join using this new similarity metrics and present a signature-based method to address this problem. We propose new signature schemes and develop effective pruning techniques to improve the performance. Experimental results show that our approach achieves high efficiency and result quality, and significantly outperforms state-of-the-art methods." @default.
• W2125980212 created "2016-06-24" @default.
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• W2125980212 date "2011-04-01" @default.
• W2125980212 modified "2023-10-18" @default.
• W2125980212 title "Fast-join: An efficient method for fuzzy token matching based string similarity join" @default.
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• W2125980212 doi "https://doi.org/10.1109/icde.2011.5767865" @default.
• W2125980212 hasPublicationYear "2011" @default.
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