Rapid sampling for visualizations with ordering guarantees
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Madden_Rapid sampling.pdf
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627.91 KB
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Author(s)
Kim, Albert
Blais, Eric
Parameswaran, Aditya
Indyk, Piotr
Rubinfeld, Ronitt
Madden, Samuel R.
Date Issued
January 2015
Journal
Proceedings of the VLDB Endowment
Publisher
Association for Computing Machinery (ACM)
Citation
Albert Kim, Eric Blais, Aditya Parameswaran, Piotr Indyk, Sam Madden, and Ronitt Rubinfeld. 2015. Rapid sampling for visualizations with ordering guarantees. Proc. VLDB Endow. 8, 5 (January 2015), 521-532.
Version
Author's final manuscript
Abstract
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 rapidly generating approximate visualizations while preserving crucial visual properties of interest to analysts. Our primary focus will be on sampling algorithms that preserve the visual property of ordering; 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License
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DOI of Published Version
https://doi.org/10.14778/2735479.2735485
