Characterizing Guidance in Visual Analytics

Teaser Image
Author
Keywords
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 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.
Year of Publication
2017
Journal
IEEE Transactions on Visualization and Computer Graphics
Volume
23
Issue
1
Number of Pages
111-120
Date Published
01/2017
Type of Article
Journal
ISSN Number
1077-2626
Citation Key
URL
http://publik.tuwien.ac.at/files/PubDat_252484.pdf
DOI
10.1109/TVCG.2016.2598468
Funding projects
Attachments