Fuzzy Joins in MapReduce: Edit and Jaccard Distance

被引：0

作者：

Kimmett, Ben ^{[1
]}

Thomo, Alex ^{[1
]}

Srinivasan, Venkatesh ^{[1
]}

机构：

[1] Univ Victoria, Victoria, BC V8W 2Y2, Canada

来源：

2016 7TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS & APPLICATIONS (IISA) | 2016年

关键词：

Fuzzy Join; Similarity Join; MapReduce; Entity Resolution; Record Linkage;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In ICDE'12, Afrati, Das Sarma, Menestrina, Parameswaran and Ullman proposed similarity join algorithms for MapReduce. In this paper, we evaluate and extend their research, testing their proposed algorithms using edit distance and Jaccard similarity. We provide details of adaptations needed to implement their algorithms based on these similarity measures. We conduct an extensive experimental study on large datasets and evaluate the algorithms across several dimensions that define the performance profile in MapReduce.

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页数：6

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