Volume

Dagstuhl Seminar Proceedings, Volume 10471



Publication Details

  • published at: 2011-02-01
  • Publisher: Schloss-Dagstuhl - Leibniz Zentrum für Informatik

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Document
10471 Abstracts Collection – Scalable Visual Analytics

Authors: Daniel A. Keim and Stefan Wrobel


Abstract
From 21.11. to 26.11.2010, the Dagstuhl Seminar 10471 ``Scalable Visual Analytics'' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

Cite as

Daniel A. Keim and Stefan Wrobel. 10471 Abstracts Collection – Scalable Visual Analytics. In Scalable Visual Analytics. Dagstuhl Seminar Proceedings, Volume 10471, pp. 1-16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2011)


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@InProceedings{keim_et_al:DagSemProc.10471.1,
  author =	{Keim, Daniel A. and Wrobel, Stefan},
  title =	{{10471 Abstracts Collection – Scalable Visual Analytics}},
  booktitle =	{Scalable Visual Analytics},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10471},
  editor =	{Daniel A. Keim and Stefan Wrobel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10471.1},
  URN =		{urn:nbn:de:0030-drops-29406},
  doi =		{10.4230/DagSemProc.10471.1},
  annote =	{Keywords: Visual Analytics, Visualization, Data Analysis, Discovery Science, Information Visualization}
}
Document
10471 Executive Summary – Scalable Visual Analytics

Authors: Daniel A. Keim and Stefan Wrobel


Abstract
The Scalable Visual Analytics seminar was a fertile meeting in which researchers from diverse backgrounds met. It included industry and academia, senior and junior researchers, multi-national representation, and people coming from several disciplines. The diversity resulted in interesting and useful discussions, which will help to shape the future of the versatile research area of Visual Analytics. The seminar included multiple presentations and discussions which helped to exchange domain knowledge and steer future research activities. Besides, several working groups during the seminar not only identified future research directions in the field of scalable visual analytics but also initiated new joint projects. In total, plans for three position papers, two overview papers to outreach to other communities, and three EU FET Open Projects were drafted. Furthermore, three workshops as satellites of conferences that cover specific application areas were planned to further disseminate the work and provide a platform for ongoing discussions and activities.

Cite as

Daniel A. Keim and Stefan Wrobel. 10471 Executive Summary – Scalable Visual Analytics. In Scalable Visual Analytics. Dagstuhl Seminar Proceedings, Volume 10471, pp. 1-5, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2011)


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@InProceedings{keim_et_al:DagSemProc.10471.2,
  author =	{Keim, Daniel A. and Wrobel, Stefan},
  title =	{{10471 Executive Summary – Scalable Visual Analytics}},
  booktitle =	{Scalable Visual Analytics},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10471},
  editor =	{Daniel A. Keim and Stefan Wrobel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10471.2},
  URN =		{urn:nbn:de:0030-drops-29393},
  doi =		{10.4230/DagSemProc.10471.2},
  annote =	{Keywords: Visual Analytics, Visualization, Data Analysis, Discovery Science, Information Visualization}
}
Document
Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics

Authors: Richard Arias-Hernández, L. Kaastra, Tera Marie Green, and Brian D. Fisher


Abstract
Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies. In this article, we introduce Pair Analytics: a method for capturing reasoning processes in visual analytics. We claim that Pair Analytics offers two advantages with respect to other methods: (1) a more natural way of making explicit and capturing reasoning processes and (2) an approach to capture social and cognitive processes used to conduct collaborative analysis in real-life settings. We support and illustrate these claims with a pilot study of three phenomena in collaborative visual analytics: coordination of attention, cognitive workload, and navigation of analysis.

Cite as

Richard Arias-Hernández, L. Kaastra, Tera Marie Green, and Brian D. Fisher. Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics. In Scalable Visual Analytics. Dagstuhl Seminar Proceedings, Volume 10471, pp. 1-10, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2011)


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@InProceedings{ariashernandez_et_al:DagSemProc.10471.3,
  author =	{Arias-Hern\'{a}ndez, Richard and Kaastra, L. and Green, Tera Marie and Fisher, Brian D.},
  title =	{{Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics}},
  booktitle =	{Scalable Visual Analytics},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10471},
  editor =	{Daniel A. Keim and Stefan Wrobel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10471.3},
  URN =		{urn:nbn:de:0030-drops-29382},
  doi =		{10.4230/DagSemProc.10471.3},
  annote =	{Keywords: Pair analytics, qualitative methods}
}

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