imMens: Real-time Visual Querying of Big Data
Article first published online: 1 JUL 2013
© 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Computer Graphics Forum
Volume 32, Issue 3pt4, pages 421–430, June 2013
How to Cite
Liu, Z., Jiang, B. and Heer, J. (2013), imMens: Real-time Visual Querying of Big Data. Computer Graphics Forum, 32: 421–430. doi: 10.1111/cgf.12129
- Issue published online: 1 JUL 2013
- Article first published online: 1 JUL 2013
- H.5.2 [Information Interfaces]: User Interfaces—
Data 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.