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Visual Analytics: Definition, Process and Challenges

Daniel Keim 1 Gennady Andrienko 2 Jean-Daniel Fekete 3 Carsten Görg 4 Jörn Kohlhammer 5 Guy Melançon 6, 7
3 AVIZ - Analysis and Visualization
Inria Saclay - Ile de France, LRI - Laboratoire de Recherche en Informatique, Université Paris-Saclay
6 GRAVITE - Graph Visualization and Interactive Exploration
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR
7 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : On a grand scale, visual analytics solutions provide technology that combines the strengths of human and electronic data processing. Visualization becomes the medium of a semi-automated analytical process, where humans and machines cooperate using their respective distinct capabilities for the most effective results. The diversity of these tasks can not be tackled with a single theory. Visual analytics research is highly interdisciplinary and combines various related research areas such as visualization, data mining, data management, data fusion, statistics and cognition science (among others).
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Contributor : Guy Melançon <>
Submitted on : Friday, April 11, 2008 - 5:40:58 PM
Last modification on : Monday, July 27, 2020 - 2:00:06 PM
Document(s) archivé(s) le : Friday, May 21, 2010 - 1:40:46 AM


  • HAL Id : lirmm-00272779, version 1


Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, et al.. Visual Analytics: Definition, Process and Challenges. Andreas Kerren and John T. Stasko and Jean-Daniel Fekete and Chris North. Information Visualization - Human-Centered Issues and Perspectives, Springer, pp.154-175, 2008, LNCS. ⟨lirmm-00272779⟩



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