Metadata
Title
imMens: Real-time Visual Querying of Big Data
Category
general
UUID
68eb42e2f56b429dbbfef4d2387a0e65
Source URL
https://idl.uw.edu/papers/immens
Parent URL
https://idl.uw.edu/papers
Crawl Time
2026-03-11T03:15:34+00:00
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imMens: Real-time Visual Querying of Big Data

Source: https://idl.uw.edu/papers/immens Parent: https://idl.uw.edu/papers

Zhicheng (Leo) Liu, Biye Jiang, Jeffrey Heer. Computer Graphics Forum (Proc. EuroVis), 2013

Zhicheng (Leo) Liu, Biye Jiang, Jeffrey Heer

Computer Graphics Forum (Proc. EuroVis), 2013

Using Google Fusion Tables (left) and imMens (right) to visualize a dataset of 4M Brightkite user checkins. Fusion Table's symbol map visualizes a sample of the data, while imMens' heatmap shows the density of checkins by aggregation. Compared to the heatmap, sampling misses important structures such as inter-state highway travel and Hurricane Ike, while dense regions still suffer from over-plotting. Moreover, imMens supports real-time brushing and linking among various dimensions of the dataset.

Materials

PDF | Software | Video

Abstract

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.

BibTeX

@article{2013-immens,
  title = {imMens: Real-time Visual Querying of Big Data},
  author = {Liu, Zhicheng AND Jiang, Biye AND Heer, Jeffrey},
  journal = {Computer Graphics Forum (Proc. EuroVis)},
  year = {2013},
  volume = {32},
  number = {3},
  url = {https://idl.uw.edu/papers/immens},
  doi = {10.1111/cgf.12129}
}

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