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This paper presents a case study of the 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. The authors 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.
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