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dc.contributor.authorKim, Albert
dc.contributor.authorBlais, Eric
dc.contributor.authorParameswaran, Aditya
dc.contributor.authorIndyk, Piotr
dc.contributor.authorRubinfeld, Ronitt
dc.contributor.authorMadden, Samuel R.
dc.date.accessioned2016-01-19T03:23:59Z
dc.date.available2016-01-19T03:23:59Z
dc.date.issued2015-01
dc.identifier.issn21508097
dc.identifier.urihttp://hdl.handle.net/1721.1/100920
dc.description.abstractVisualizations 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.en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.14778/2735479.2735485en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en_US
dc.sourceACMen_US
dc.titleRapid sampling for visualizations with ordering guaranteesen_US
dc.typeArticleen_US
dc.identifier.citationAlbert 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.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorKim, Alberten_US
dc.contributor.mitauthorBlais, Ericen_US
dc.contributor.mitauthorParameswaran, Adityaen_US
dc.contributor.mitauthorIndyk, Piotren_US
dc.contributor.mitauthorMadden, Samuel R.en_US
dc.contributor.mitauthorRubinfeld, Ronitten_US
dc.relation.journalProceedings of the VLDB Endowmenten_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsKim, Albert; Blais, Eric; Parameswaran, Aditya; Indyk, Piotr; Madden, Sam; Rubinfeld, Ronitten_US
dc.identifier.orcidhttps://orcid.org/0000-0002-7470-3265
dc.identifier.orcidhttps://orcid.org/0000-0002-4353-7639
dc.identifier.orcidhttps://orcid.org/0000-0002-7983-9524
dc.identifier.orcidhttps://orcid.org/0000-0002-3106-466X
mit.licensePUBLISHER_CCen_US


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