imMens: Real‐time Visual Querying of Big Data

By Zhicheng Liu, Biye Jiang, Jeffrey Heer. Computer graphics forum (Print), 2013

395 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Controlling False Discoveries During Interactive Data Exploration (2016, methodology): “Similar workflows however can be achieved with other systems like Tableau [17], imMens [26] or Dice [23].”
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “Other modern approaches have also been proposed in literature taking advantage of technological developments such as cloud computing , parallel processing , indexing and querying for real time utilization (e.g., Chen et al. 2014, Hoffman 2012, Liu et al. 2013, Lu et al. 2013).”
Data visualization (2015, background): “Data visualization promise to provide the advantages that companies need to run revenue growth and gain a competitive edge [2].”
Handbook of Big Data (2016, methodology): “have created imMens, a JavaScript-based library that leverages the parallel processing of graphics processing units (GPUs) to very quickly compute aggregate sums over partial data cubes [17].”
Visual exploration of machine learning results using data cube analysis (2016, methodology): “title_length [0, 3) [3, 6) [6, 9) [9, 12) [12, 15) [15, 18) [18, 21) [21, 24)”