Visual Analytics: Definition, Process, and Challenges

  • Authors:

  • Daniel Keim;Gennady Andrienko;Jean-Daniel Fekete;Carsten Görg;Jörn Kohlhammer;Guy Melançon

  • Affiliations:

  • Department of Computer and Information Science, University of Konstanz, Konstanz, Germany 78457;Fraunhofer Institute for Intelligent Analysis and Information Systems(IAIS), Sankt Augustin, Germany 53754;INRIA, Université Paris-Sud, Orsay Cedex, France F-91405;School of Interactive Computing & GVU Center, Georgia Institute of Technology, Atlanta, USA GA 30332-0760;Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany D-64283;INRIA Bordeaux --- Sud-Ouest, CNRS UMR 5800 LaBRI, Talence Cedex, France 33405

  • Venue:
  • Information Visualization
  • Year:
  • 2008

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Abstract

We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way influenced our way of dealing with information: Most of the time, data is stored without filtering and refinement for later use. Virtually every branch of industry or business, and any political or personal activity nowadays generate vast amounts of data. Making matters worse, the possibilities to collect and store data increase at a faster rate than our ability to use it for making decisions. However, in most applications, raw data has no value in itself; instead we want to extract the information contained in it.