<div class="csl-bib-body">
<div class="csl-entry">Ceneda, D., Gschwandtner, T., May, T., Miksch, S., Schulz, H.-J., Streit, M., & Tominski, C. (2016). <i>Characterizing Guidance in Visual Analytics</i> (p. 120). http://hdl.handle.net/20.500.12708/86330</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/86330
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dc.description.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.
en
dc.language.iso
en
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dc.subject
Software
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dc.subject
Visual analytics
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dc.subject
Computer Graphics and Computer-Aided Design
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dc.subject
Computer Vision and Pattern Recognition
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dc.subject
Signal Processing
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dc.subject
guidance model
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dc.subject
assistance
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dc.subject
user support
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dc.title
Characterizing Guidance in Visual Analytics
en
dc.type
Präsentation
de
dc.type
Presentation
en
dc.description.endpage
120
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dc.type.category
Poster Presentation
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tuw.peerreviewed
false
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tuw.publication.orgunit
E193-07 - Forschungsbereich Visual Analytics
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dc.description.numberOfPages
10
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tuw.event.name
IEEE Conference on Visual Analytics Science and Technology (IEEE VAST)
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tuw.event.startdate
13-10-2013
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tuw.event.enddate
18-10-2013
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Atlanta
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tuw.event.country
US
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tuw.event.presenter
Ceneda, Davide
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.facultyfocus
Visual Computing and Human-Centered Technology (VC + HCT)
de
wb.facultyfocus
Visual Computing and Human-Centered Technology (VC + HCT)
en
wb.facultyfocus.faculty
E180
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item.openairetype
conference poster not in proceedings
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.languageiso639-1
en
-
item.openairecristype
http://purl.org/coar/resource_type/c_18co
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item.grantfulltext
restricted
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crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
-
crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
-
crisitem.author.dept
E193-07 - Forschungsbereich Visual Analytics
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crisitem.author.orcid
0000-0003-1198-567X
-
crisitem.author.orcid
0000-0003-4427-5703
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
