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SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data
November/December 2009 (vol. 15 no. 6)
pp. 1025-1032
Zhicheng Liu, Georgia Institute of Technology
John Stasko, Georgia Institute of Technology
Timothy Sullivan, Travelport Corp.
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.

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Index Terms:
investigative analysis, transaction analysis, information visualization, multiple views, time series data, multiple attributes, categorical data
Zhicheng Liu, John Stasko, Timothy Sullivan, "SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1025-1032, Nov.-Dec. 2009, doi:10.1109/TVCG.2009.180
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