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Characterizing guidance in visual analytics

: Ceneda, Davide; Gschwandtner, Theresia; May, Thorsten; Miksch, Silvia; Schulz, Hans-Jörg; Streit, Marc; Tominski, Christian


IEEE transactions on visualization and computer graphics 23 (2017), Nr.1, S.111-120
ISSN: 1077-2626
Conference on Visual Analytics Science and Technology (VAST) <11, 2016, Baltimore/Md.>
Conference on Visualization (VIS) <2016, Baltimore/Md.>
Zeitschriftenaufsatz, Konferenzbeitrag
Fraunhofer IGD ()
Visual analytics; assistance; user support; user guidance; Guidance models; Guiding Theme: Digitized Work; Guiding Theme: Visual Computing as a Service; Research Area: Human computer interaction (HCI)

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.