imMens: Real-time Visual Querying of Big Data

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,...

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Bibliographic Details
Published in:Computer graphics forum 2013-06, Vol.32 (3pt4), p.421-430
Main Authors: Liu, Zhicheng, Jiang, Biye, Heer, Jeffrey
Format: Article
Language:English
Subjects:
Analysis
Big Data
Brushing
Computer graphics
Data analysis
H.5.2 [Information Interfaces]: User Interfaces
Information systems
Interactive
Invariants
Joining
Linking
Sampling
Scalability
Studies
Visual
Visualization
Web browsers
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Summary: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.
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12129