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<p><tt><font size="2"><b>The 3rd IEEE Workshop on Interactive Visual Text Analytics:<br>
Integrated Analysis of Heterogeneous Data and Ubiquitous Text Analytics<br>
</b></font></tt><tt><font size="2"><b>www.textvis.org</b></font></tt><tt><font size="2"><br>
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</font></tt><tt><font size="2"><b>IEEE VIS 2013<br>
Atlanta, Georgia<br>
October 2013<br>
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</font></tt><tt><font size="2"><b>Workshop Goals</b></font></tt><tt><font size="2"><br>
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Much research has been reported on visual text analytics for plain text<br>
documents viewed in traditional analytic settings. In this workshop, we<br>
would like to push the boundary of visual text analytics toward<br>
heterogeneous textual data (text associated with other data types) and<br>
ubiquitous text analytics. First, we would like to use the workshop to<br>
collect various use cases about heterogeneous data and ubiquitous text<br>
analytics. From the use cases, we hope to better understand the<br>
requirements of heterogeneous textual data analysis from a task-driven<br>
perspective. Although there is some work on visual analysis of<br>
heterogeneous textual data, there is not a clear understanding of the<br>
typical tasks that people would like to achieve in analyzing heterogeneous<br>
textual data. Moreover, how will different tasks influence the design and<br>
development of both text analytics and visualization technologies? We thus<br>
would like to leverage the power of the crowd at the workshop to examine a<br>
number of use cases and draft a taxonomy that characterizes the design<br>
dimensions of the space and can also be used to guide the future design and<br>
development. Second, based on the use cases, we would like to use this<br>
workshop to examine how to best leverage state-of-the-art text analytics<br>
and traditional data mining techniques in conjunction with novel<br>
interactive visual analytics to address the challenges manifested by the<br>
collected use cases.<br>
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Paper Submission</b></font></tt><tt><font size="2"><br>
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We invite 2-4 page position paper submissions that address topics related<br>
to interactive visual analysis of heterogeneous textual data or visual<br>
analysis of text data on mobile devices or other ubiquitous scenarios, with<br>
a requirement that every submission must clearly state one or more concrete<br>
use cases, including the tasks to be achieved, the two or more types of<br>
data to be analyzed, and data analytic methods used. Topics of interest<br>
include but are not limited to:<br>
<br>
· Task taxonomy of analysis of heterogeneous textual data or ubiquitous<br>
text analytics<br>
· Visual metaphors for heterogeneous textual data or ubiquitous text<br>
analytics<br>
· Coordinated visualizations of textual and non-textual data<br>
· Perception and cognition in heterogeneous data visualization or<br>
ubiquitous text analytics<br>
· Mobile visual text analytics and mobile visual interaction<br>
· Systems, languages, and architectures for heterogeneous textual data<br>
analysis<br>
· Collaborative analysis of heterogeneous textual data<br>
· Real-time visualization of streaming heterogeneous textual data<br>
· Opinion summarization from heterogeneous textual data<br>
· Visual event identification and prediction from heterogeneous textual<br>
data<br>
· Uncertainty in interactive heterogeneous textual data analysis or<br>
ubiquitous text analytics<br>
· Industry-specific applications of visual analysis of heterogeneous<br>
textual data (e.g. Retail, Healthcare, Government, etc.)<br>
· Studies and evaluation of heterogeneous textual data visualization<br>
techniques, systems, metrics, and bench-marks<br>
· Datasets and tasks for visual text analysis of heterogeneous textual<br>
data<br>
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Important dates</b></font></tt><tt><font size="2"><br>
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August 31st: Position paper due<br>
September 14th: Notification<br>
October 1st: Final version of papers due<br>
October 14th: Visual text analytics workshop<br>
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Please submit your position papers by email to: textvis@us.ibm.com<br>
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</font></tt><tt><font size="2"><b>Organizers</b></font></tt><tt><font size="2"><br>
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Chris Collins, University of Ontario Institute of Technology<br>
Eser Kandogan, IBM Research, Almaden<br>
Shixia Liu, Microsoft Research Asia<br>
Michelle Zhou, IBM Research, Almaden<br>
Chad Steed, Oakridge National Lab<br>
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