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  4. Characterizing guidance in visual analytics
 
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2017
Journal Article
Title

Characterizing guidance in visual analytics

Abstract
Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools.
Author(s)
Ceneda, Davide
TU Wien
Gschwandtner, Theresia
TU Wien
May, Thorsten  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Miksch, Silvia
TU Wien
Schulz, Hans-Jörg
Univ. Rostock
Streit, Marc
Univ. Linz
Tominski, Christian
Univ. Rostock
Journal
IEEE transactions on visualization and computer graphics  
Conference
Conference on Visual Analytics Science and Technology (VAST) 2016  
Conference on Visualization (VIS) 2016  
DOI
10.1109/TVCG.2016.2598468
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Visual analytics

  • assistance

  • user support

  • user guidance

  • Guidance models

  • Lead Topic: Digitized Work

  • Lead Topic: Visual Computing as a Service

  • Research Line: Human computer interaction (HCI)

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