NDLI: Scalable histograms on large probabilistic data

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Scalable histograms on large probabilistic data

Content Provider	ACM Digital Library
Author	Tang, Mingwang Li, Feifei
Abstract	Histogram construction is a fundamental problem in data management, and a good histogram supports numerous mining operations. Recent work has extended histograms to probabilistic data. However, constructing histograms for probabilistic data can be extremely expensive, and existing studies suffer from limited scalability. This work designs novel approximation methods to construct scalable histograms on probabilistic data. We show that our methods provide constant approximations compared to the optimal histograms produced by the state-of-the-art in the worst case. We also extend our methods to parallel and distributed settings so that they can run gracefully in a cluster of commodity machines. We introduced novel synopses to reduce communication cost when running our methods in such settings. Extensive experiments on large real data sets have demonstrated the superb scalability and efficiency achieved by our methods, when compared to the state-of-the-art methods. They also achieved excellent approximation quality in practice.
Starting Page	631
Ending Page	640
Page Count	10
File Format	PDF MP4
ISBN	9781450329569
DOI	10.1145/2623330.2623640
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Histogram Scalable method Probabilistic database
Content Type	Audio Text
Resource Type	Article

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