imMens: Real-time Visual Querying of Big Data

dc.contributor.authorLiu, Zhichengen_US
dc.contributor.authorJiang, Biyeen_US
dc.contributor.authorHeer, Jeffreyen_US
dc.contributor.editorB. Preim, P. Rheingans, and H. Theiselen_US
dc.date.accessioned2015-02-28T15:31:44Z
dc.date.available2015-02-28T15:31:44Z
dc.date.issued2013en_US
dc.description.abstractData analysts must make sense of increasingly large data sets, sometimes with billions or more records.We present methods for interactive visualization of big data, following the principle that perceptual and interactive scalability should be limited by the chosen resolution of the visualized data, not the number of records. We first describe a design space of scalable visual summaries that use data reduction methods (such as binned aggregation or sampling) to visualize a variety of data types. We then contribute methods for interactive querying (e.g., brushing & linking) among binned plots through a combination of multivariate data tiles and parallel query processing. We implement our techniques in imMens, a browser-based visual analysis system that uses WebGL for data processing and rendering on the GPU. In benchmarks imMens sustains 50 frames-per-second brushing & linking among dozens of visualizations, with invariant performance on data sizes ranging from thousands to billions of records.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12129en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12129en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectH.5.2 [Information Interfaces]en_US
dc.subjectUser Interfacesen_US
dc.titleimMens: Real-time Visual Querying of Big Dataen_US
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