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<p><font size="2" face="sans-serif"><b>The 3rd IEEE Workshop on Interactive Visual Text Analytics: </b></font><br>
<font size="2" face="sans-serif"><b>Integrated Analysis of Heterogeneous Data and Ubiquitous Text Analytics</b></font><br>
<font size="2" color="#0000FF" face="sans-serif"><b>www.textvis.org</b></font><br>
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<font size="3" face="serif"><b>IEEE VIS 2013</b></font><br>
<font size="3" face="serif"><b>Atlanta, Georgia</b></font><br>
<font size="3" face="serif"><b>October 2013</b></font>
<p><font size="1" face="Arial"><b>Workshop Goals </b></font>
<p><font size="1" face="Times">Much research has been reported on visual text analytics for plain text documents viewed in traditional analytic settings. In this workshop, we would like to push the boundary of visual text analytics toward heterogeneous textual data (text associated with other data types) and ubiquitous text analytics. First, we would like to use the workshop to collect various use cases about heterogeneous data and ubiquitous text analytics. From the use cases, we hope to better understand the requirements of heterogeneous textual data analysis from a task-driven perspective. Although there is some work on visual analysis of heterogeneous textual data, there is not a clear understanding of the typical tasks that people would like to achieve in analyzing heterogeneous textual data. Moreover, how will different tasks influence the design and development of both text analytics and visualization technologies? We thus would like to leverage the power of the crowd at the workshop to examine a number of use cases and draft a taxonomy that characterizes the design dimensions of the space and can also be used to guide the future design and development. Second, based on the use cases, we would like to use this workshop to examine how to best leverage state-of-the-art text analytics and traditional data mining techniques in conjunction with novel interactive visual analytics to address the challenges manifested by the collected use cases. </font><br>
<p><font size="1" face="Arial"><b>Paper Submission </b></font>
<p><font size="1" face="Times">We invite 2-4 page position paper submissions that address topics related to interactive visual analysis of heterogeneous textual data or visual analysis of text data on mobile devices or other ubiquitous scenarios, with a requirement that </font><font size="1" face="Times"><b><i>every</i></b></font><font size="1" face="Times"> submission must clearly state one or more </font><font size="1" face="Times"><b><i>concrete use cases</i></b></font><font size="1" face="Times">, including the tasks to be achieved, the two or more types of data to be analyzed, and data analytic methods used. Topics of interest include but are not limited to:</font><br>
<ul style="padding-left: 8pt"><font size="1" face="Symbol">· </font><font size="1" face="Times">Task taxonomy of analysis of heterogeneous textual data or ubiquitous text analytics</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Visual metaphors for heterogeneous textual data or ubiquitous text analytics</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Coordinated visualizations of textual and non-textual data</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Perception and cognition in heterogeneous data visualization or ubiquitous text analytics</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Mobile visual text analytics and mobile visual interaction</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Systems, languages, and architectures for heterogeneous textual data analysis</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Collaborative analysis of heterogeneous textual data</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Real-time visualization of streaming heterogeneous textual data </font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Opinion summarization from heterogeneous textual data</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Visual event identification and prediction from heterogeneous textual data</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Uncertainty in interactive heterogeneous textual data analysis or ubiquitous text analytics</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Industry-specific applications of visual analysis of heterogeneous textual data (e.g. Retail, Healthcare, Government, etc.)</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Studies and evaluation of heterogeneous textual data visualization techniques, systems, metrics, and bench-marks</font><br>
<font size="1" face="Symbol">· </font><font size="1" face="Times">Datasets and tasks for visual text analysis of heterogeneous textual data</font></ul>
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<font size="1" face="Arial"><b>Important dates</b></font>
<p><font size="1" face="Times"><b>August 31</b></font><font size="1" face="Times"><b><sup>st</sup></b></font><font size="1" face="Times">: Position paper due</font><br>
<font size="1" face="Times"><b>September 14</b></font><font size="1" face="Times"><b><sup>th</sup></b></font><font size="1" face="Times">: Notification</font><br>
<font size="1" face="Times"><b>October 1</b></font><font size="1" face="Times"><b><sup>st</sup></b></font><font size="1" face="Times">: Final version of papers due</font><br>
<font size="1" face="Times"><b>October 14</b></font><font size="1" face="Times"><b><sup>th</sup></b></font><font size="1" face="Times">: Visual text analytics workshop</font><br>
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<font size="1" face="Times">Please submit your position papers by email to: </font><font size="1" face="Times"><b>textvis@us.ibm.com</b></font><br>
<p><font size="1" face="Times"><b>Organizers</b></font>
<p><font size="1" face="Times">Chris Collins, University of Ontario Institute of Technology</font><br>
<font size="1" face="Times">Eser Kandogan, IBM Research, Almaden</font><br>
<font size="1" face="Times">Shixia Liu, Microsoft Research Asia</font><br>
<font size="1" face="Times">Michelle Zhou, IBM Research, Almaden</font><br>
<font size="1" face="Times">Chad Steed, Oakridge National Lab</font>
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