Characterizing Guidance in Visual Analytics

TitleCharacterizing Guidance in Visual Analytics
Publication TypeJournal Article
Year of Publication2017
AuthorsCeneda, D., T. Gschwandtner, T. May, S. Miksch, H. - J. Schulz, M. Streit, and C. Tominski
JournalIEEE Transactions on Visualization and Computer Graphics
Date Published01/2017
Type of ArticleJournal
Keywordsassistance, guidance model, user support, Visual analytics
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 analytic goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist analysts throughout 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.
In this work, 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. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.
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