Rapid Sampling for Visualizations with Ordering Guarantees

Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of . Our primary focus will be on sampling algorithms that preserve the visual property of ; our techniques will also a...

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Bibliographic Details
Published in:Proceedings of the VLDB Endowment 2015-01, Vol.8 (5), p.521-532
Main Authors: Kim, Albert, Blais, Eric, Parameswaran, Aditya, Indyk, Piotr, Madden, Sam, Rubinfeld, Ronitt
Format: Article
Language:English
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Summary:Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of . Our primary focus will be on sampling algorithms that preserve the visual property of ; our techniques will also apply to some other visual properties. For instance, our algorithms can be used to generate an approximate visualization of a bar chart very rapidly, where the comparisons between any two bars are correct. We formally show that our sampling algorithms are generally applicable and provably optimal in theory, in that they do not take more samples than necessary to generate the visualizations with ordering guarantees. They also work well in practice, correctly ordering output groups while taking orders of magnitude fewer samples and much less time than conventional sampling schemes.
ISSN:2150-8097
2150-8097
DOI:10.14778/2735479.2735485