2013 ScalableAllPairsSimilaritySearc

• (Wang et al., 2013) ⇒ Ye Wang, Ahmed Metwally, and Srinivasan Parthasarathy. (2013). “Scalable all-pairs Similarity Search in Metric Spaces.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487625

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Scalable+all-pairs+Similarity+Search+in+Metric+Spaces
• http://dl.acm.org/citation.cfm?id=2487575.2487625&preflayout=flat#citedby

Quotes

Author Keywords

• All-pairs similarity search; data mining; similarity joins

Abstract

Given a set of entities, the all-pairs similarity search aims at identifying all pairs of entities that have similarity greater than (or distance smaller than) some user-defined threshold. In this article, we propose a parallel framework for solving this problem in metric spaces. Novel elements of our solution include: i) flexible support for multiple metrics of interest; ii) an autonomic approach to partition the input dataset with minimal redundancy to achieve good load-balance in the presence of limited computing resources; iii) an on-the-fly lossless compression strategy to reduce both the running time and the final output size. We validate the utility, scalability and the effectiveness of the approach on hundreds of machines using real and synthetic datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ScalableAllPairsSimilaritySearc	Srinivasan Parthasarathy Ahmed Metwally Ye Wang			Scalable all-pairs Similarity Search in Metric Spaces				10.1145/2487575.2487625		2013