Paper Info
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. 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.
Year
DOI
Venue
2017
10.1109/TVCG.2016.2598468
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
Context modeling,Context,Automobiles,Visual analytics,Data visualization
Computer vision,Data visualization,Visualization,Computer science,Continuation,Visual analytics,Complex data type,Context model,Human–computer interaction,Artificial intelligence
Journal
Volume
Issue
ISSN
23
1
1077-2626
Citations 
PageRank 
References 
35
0.95
40
Authors
7
Name
Order
Citations
PageRank
Davide Ceneda1473.82
Theresia Gschwandtner217117.43
thorsten may317315.75
Silvia Miksch42212174.85
Hans-Jörg Schulz550928.80
Marc Streit654928.91
Christian Tominski796446.77