Information Visualization pp 154-175

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4950) | Cite as

Visual Analytics: Definition, Process, and Challenges

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

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Daniel Keim
    • 1
  • Gennady Andrienko
    • 2
  • Jean-Daniel Fekete
    • 3
  • Carsten Görg
    • 4
  • Jörn Kohlhammer
    • 5
  • Guy Melançon
    • 6
  1. 1.Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
  2. 2.Fraunhofer Institute for Intelligent Analysis and Information Systems(IAIS)Sankt AugustinGermany
  3. 3.INRIAUniversité Paris-SudOrsay CedexFrance
  4. 4.School of Interactive Computing & GVU CenterGeorgia Institute of TechnologyAtlantaUSA
  5. 5.Fraunhofer Institute for Computer Graphics ResearchDarmstadtGermany
  6. 6.INRIA Bordeaux – Sud-Ouest, CNRS UMR 5800 LaBRITalence CedexFrance

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