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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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This Brown Bag reviews papers and posters presented by GT researchers
during the InfoVis Conference and the Visual Analytics Symposium at
IEEE VisWeek, October 12-16 in Atlantic City. The following articles
will be presented:
"SellTrend: Inter-Attribute Visual Analysis of Temporal
Transaction Data" (InfoVis full paper, Honorable Mention Best Paper)
AUTHORS: Zhicheng Liu, John Stasko, Timothy Sullivan
ABSTRACT: We present a case study of our experience designing
SellTrend, a visualization system for analyzing airline travel purchase
requests. The relevant transaction data can be characterized as
multi-variate temporal and categorical event sequences, and the chief
problem addressed is how to help company analysts identify complex
combinations of transaction attributes that contribute to failed
purchase requests. SellTrend combines a diverse set of techniques
ranging from time series visualization to faceted browsing and
historical trend analysis in order to help analysts make sense of the
data. We believe that the combination of views and interaction
capabilities in SellTrend provides an innovative approach to this
problem and to other similar types of multivariate, temporally-driven
transaction data analysis. Initial feedback from company analysts
confirms the utility and benefits of the system.
"Evaluating Visual Analytics Systems for Investigative Analysis:
Deriving Design Principles from a Case Study" (VAST full paper)
AUTHORS: Youn-ah Kang, Carsten Gorg, John Stasko
ABSTRACT: Despite the growing number of systems providing visual
analytic support for investigative analysis, few empirical studies of
the potential benefits of such systems have been conducted,
particularly controlled, comparative evaluations. Determining how such
systems foster insight and sensemaking is important for their continued
growth and study, however. Furthermore, studies that identify how
people use such systems and why they benefit (or not) can help inform
the design of new systems in this area. We conducted an evaluation of
the visual analytics system Jigsaw employed in a small investigative
sensemaking exercise, and we compared its use to three other more
traditional methods of analysis. Sixteen participants performed a
simulated intelligence analysis task under one of the four conditions.
Experimental results suggest that Jigsaw assisted participants to
analyze the data and identify an embedded threat. We describe different
analysis strategies used by study participants and how computational
support (or the lack thereof) influenced the strategies. We then
illustrate several characteristics of the sensemaking process
identified in the study and provide design implications for
investigative analysis tools based thereon. We conclude with
recommendations for metrics and techniques for evaluating other visual
analytics investigative analysis tools.
"Two-stage Framework for Visualization of Clustered High Dimensional Data" (VAST full paper)
AUTHORS: Jaegul Choo, Shawn Bohn, Haesun Park
ABSTRACT: In this paper, we discuss dimension reduction methods for 2D
visualization of high dimensional clustered data. We propose a twostage
framework for visualizing such data based on dimension reduction
methods. In the first stage, we obtain the reduced dimensional data by
applying a supervised dimension reduction method such as linear
discriminant analysis which preserves the original cluster structure in
terms of its criteria. The resulting optimal reduced dimension depends
on the optimization criteria and is often larger than 2. In the second
stage, the dimension is further reduced to 2 for visualization purposes
by another dimension reduction method such as principal component
analysis. The role of the second-stage is to minimize the loss of
information due to reducing the dimension all the way to 2. Using this
framework, we propose several two-stage methods, and present their
theoretical characteristics as well as experimental comparisons on both
artificial and real-world text data sets.
"Social Visualization for Micro-Blogging Analysis" (InfoVis poster)
AUTHORS: Tanyoung Kim, Hee Young Jeong, Yee Chieh Chew, Matthew Bonner, John Stasko
"Interactive Visualization of Ecosystem Change for Public Education" (InfoVis poster)
AUTHORS: Tanyoung Kim, Hwajung Hong, Brian Magerko
"Perspectives on Time: Enhancing Utility with Flexibility" (InfoVis poster)
AUTHORS: Peter Kinnaird, John Stasko
"Connect to Congress" (InfoVis poster)
AUTHORS: by Peter Kinnaird, Hafez Rouzati, Xin Sun
