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  <title><![CDATA[GVU BROWN BAG: InfoVis and Visual Analytics Review Talks]]></title>
  <body><![CDATA[<p>This Brown Bag reviews papers and posters presented by GT researchers<br />
during the InfoVis Conference and the Visual Analytics Symposium at<br />
IEEE VisWeek, October 12-16 in Atlantic City. The following articles<br />
will be presented:</p>
<p>"SellTrend: Inter-Attribute Visual Analysis of Temporal<br />
Transaction Data" (InfoVis full paper, Honorable Mention Best Paper)<br />
<br />
AUTHORS:  Zhicheng Liu, John Stasko, Timothy Sullivan</p>
<p>ABSTRACT: We present a case study of our experience designing<br />
SellTrend, a visualization system for analyzing airline travel purchase<br />
requests. The relevant transaction data can be characterized as<br />
multi-variate temporal and categorical event sequences, and the chief<br />
problem addressed is how to help company analysts identify complex<br />
combinations of transaction attributes that contribute to failed<br />
purchase requests. SellTrend combines a diverse set of techniques<br />
ranging from time series visualization to faceted browsing and<br />
historical trend analysis in order to help analysts make sense of the<br />
data. We believe that the combination of views and interaction<br />
capabilities in SellTrend provides an innovative approach to this<br />
problem and to other similar types of multivariate, temporally-driven<br />
transaction data analysis. Initial feedback from company analysts<br />
confirms the utility and benefits of the system.</p>
<p>"Evaluating Visual Analytics Systems for Investigative Analysis:<br />
Deriving Design Principles from a Case Study" (VAST full paper)<br />
<br />
AUTHORS:  Youn-ah Kang, Carsten Gorg, John Stasko </p>
<p>ABSTRACT: Despite the growing number of systems providing visual<br />
analytic support for investigative analysis, few empirical studies of<br />
the potential benefits of such systems have been conducted,<br />
particularly controlled, comparative evaluations. Determining how such<br />
systems foster insight and sensemaking is important for their continued<br />
growth and study, however. Furthermore, studies that identify how<br />
people use such systems and why they benefit (or not) can help inform<br />
the design of new systems in this area. We conducted an evaluation of<br />
the visual analytics system Jigsaw employed in a small investigative<br />
sensemaking exercise, and we compared its use to three other more<br />
traditional methods of analysis. Sixteen participants performed a<br />
simulated intelligence analysis task under one of the four conditions.<br />
Experimental results suggest that Jigsaw assisted participants to<br />
analyze the data and identify an embedded threat. We describe different<br />
analysis strategies used by study participants and how computational<br />
support (or the lack thereof) influenced the strategies. We then<br />
illustrate several characteristics of the sensemaking process<br />
identified in the study and provide design implications for<br />
investigative analysis tools based thereon. We conclude with<br />
recommendations for metrics and techniques for evaluating other visual<br />
analytics investigative analysis tools.</p>
<p>"Two-stage Framework for Visualization of Clustered High Dimensional Data" (VAST full paper)<br />
<br />
AUTHORS:  Jaegul Choo, Shawn Bohn, Haesun Park </p>
<p>ABSTRACT: In this paper, we discuss dimension reduction methods for 2D<br />
visualization of high dimensional clustered data. We propose a twostage<br />
framework for visualizing such data based on dimension reduction<br />
methods. In the first stage, we obtain the reduced dimensional data by<br />
applying a supervised dimension reduction method such as linear<br />
discriminant analysis which preserves the original cluster structure in<br />
terms of its criteria. The resulting optimal reduced dimension depends<br />
on the optimization criteria and is often larger than 2. In the second<br />
stage, the dimension is further reduced to 2 for visualization purposes<br />
by another dimension reduction method such as principal component<br />
analysis. The role of the second-stage is to minimize the loss of<br />
information due to reducing the dimension all the way to 2. Using this<br />
framework, we propose several two-stage methods, and present their<br />
theoretical characteristics as well as experimental comparisons on both<br />
artificial and real-world text data sets.</p>
<p>"Social Visualization for Micro-Blogging Analysis" (InfoVis poster)<br />
<br />
AUTHORS:  Tanyoung Kim, Hee Young Jeong, Yee Chieh Chew, Matthew Bonner, John Stasko</p>
<p>"Interactive Visualization of Ecosystem Change for Public Education" (InfoVis poster)<br />
<br />
AUTHORS: Tanyoung Kim, Hwajung Hong, Brian Magerko </p>
<p>"Perspectives on Time: Enhancing Utility with Flexibility" (InfoVis poster)<br />
<br />
AUTHORS:  Peter Kinnaird, John Stasko </p>
<p>"Connect to Congress" (InfoVis poster)<br />
<br />
AUTHORS:  by Peter Kinnaird, Hafez Rouzati, Xin Sun</p>]]></body>
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