Abstract
In numerous application areas fast growing data sets develop with ever higher complexity and dynamics. A central challenge is to filter the substantial information and to communicate it to humans in an appropriate way. Approaches, which work either on a purely analytical or on a purely visual level, do not sufficiently help due to the dynamics and complexity of the underlying processes or due to a situation with intelligent opponents. Only a combination of data analysis and visualization techniques make an effective access to the otherwise unmanageably complex data sets possible.
Visual analysis techniques extend the perceptual and cognitive abilities of humans with automatic data analysis techniques, and help to gain insights for optimizing and steering complicated processes. In the paper, we introduce the basic idea of Visual Analytics, explain how automated discovery and visual analysis methods can be combined, discuss the main challenges of Visual Analytics, and show that combining automatic and visual analysis is the only chance to capture the complex, changing characteristics of the data. To further explain the Visual Analytics process, we provide examples from the area of document analysis.
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Keim, D.A., Mansmann, F., Oelke, D., Ziegler, H. (2008). Visual Analytics: Combining Automated Discovery with Interactive Visualizations. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_2
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DOI: https://doi.org/10.1007/978-3-540-88411-8_2
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