title: Rapid Sampling for Visualizations with Ordering Guarantees.
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 ofrapidly 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 ofordering; 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.
authors.affiliation: MIT.
authors.forename: Albert
authors.initials: A
authors.lastname: Kim
authors.affiliation: MIT and University of Waterloo.
authors.forename: Eric
authors.initials: E
authors.lastname: Blais
authors.affiliation: MIT and Illinois (UIUC).
authors.forename: Aditya
authors.initials: A
authors.lastname: Parameswaran
authors.affiliation: MIT.
authors.forename: Piotr
authors.initials: P
authors.lastname: Indyk
authors.affiliation: MIT.
authors.forename: Sam
authors.initials: S
authors.lastname: Madden
authors.affiliation: MIT and Tel Aviv University.
authors.forename: Ronitt
authors.initials: R
authors.lastname: Rubinfeld