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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.

[1] W. Aigner, S. Miksch, W. Mller, H. Schumann, and C. Tominski, Visual methods for analyzing Time-Oriented data. IEEE Transactions on Visualization and Computer Graphics, pages 47–60, 2008.
[2] B. B. Bederson, B. Shneiderman, and M. Wattenberg, Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Transactions on Graphics, 21 (4): 833–854, 2002.
[3] B. B. Bederson and M. Wattenberg, Java 1.1 Library of Five Treemap Algorithms. http://www.cs.umd.edu/hcil/treemap-history .
[4] F. Bendix, R. Kosara, and H. Hauser, Parallel sets: Visual analysis of categorical data. In Proceedings of the IEEE Symposium on Information Visualization, pages 133–140, 2005.
[5] C. A. Brewer, Color use guidelines for data representation. In Proceedings of the Section on Statistical Graphics, American Statistical Association, pages 55–60, 1999.
[6] M. Bruls, K. Huizing, and J. J. van Wijk, Squarified treemaps. In Proceedings of the Joint Eurographics and IEEE TCVG Symposium on Visualization, pages 33–42, 2000.
[7] R. Chang, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto, WireVis: visualization of categorical, Time-Varying data from financial transactions. In IEEE Symposium on Visual Analytics Science and Technology, pages 155–162, 2007.
[8] G. Conti, K. Abdullah, J. Grizzard, J. Stasko, J. A. Copeland, M. Ahamad, H. L. Owen, and C. Lee, Countering security information overload through alert and packet visualization. IEEE Computer Graphics and Applications, pages 60–70, 2006.
[9] R. Dachselt, M. Frisch, and M. Weiland, FacetZoom: a continuous multi-scale widget for navigating hierarchical metadata. In Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1353–1356, Florence, Italy, 2008. ACM.
[10] A. Dix and G. Ellis, Starting simple: adding value to static visualisation through simple interaction. In Proceedings of the working conference on Advanced Visual Interfaces, pages 124–134, 1998.
[11] S. Havre, E. Hetzler, P. Whitney, and L. Nowell, ThemeRiver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, pages 9–20, 2002.
[12] E. L. Hutchins, J. D. Hollan, and D. A. Norman, Direct manipulation interfaces. Human-Computer Interaction, 1 (4): 311–338, 1985.
[13] A. Inselberg, Multidimensional detective. In IEEE Symposium on Information Visualization, pages 100–107, 1997.
[14] B. Johnson and B. Shneiderman, Tree-Maps: a space-filling approach to the visualization of hierarchical information structures. In Proceedings of the 2nd conference on Visualization, pages 284–291, 1991.
[15] D. A. Keim, M. C. Hao, U. Dayal, and M. Hsu, Pixel bar charts: a visualization technique for very large multi-attribute data sets. Information Visualization, 1 (1): 20–34, 2002.
[16] A. Khan, Java Excel API. http://www.andykhan.com/jexcelapiindex.html, . Oct. 2008.
[17] B. Lee, G. Smith, G. Robertson, M. Czerwinski, and D. S. Tan, FacetLens: exposing trends and relationships to support sensemaking within faceted datasets. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Apr. 2009.
[18] Z. Liu, N. J. Nersessian, and J. T. Stasko, Distributed cognition as a theoretical framework for information visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1173–1180, Dec. 2008.
[19] C. Plaisant, B. Milash, A. Rose, S. Widoff, and B. Shneiderman, Life-Lines: visualizing personal histories. Proceedings of the SIGCHI conference on Human factors in computing systems, 1996.
[20] G. Smith, M. Czerwinski, B. Meyers, D. Robbins, G. Robertson, and D. S. Tan, FacetMap: a scalable search and browse visualization. IEEE Transactions on Visualization and Computer Graphics 12: 797– 804, 2006.
[21] R. Spence and L. Tweedie, The attribute explorer: information synthesis via exploration. Interacting with Computers, 11 (2): 137–146, 1998.
[22] M. Spenke and C. Beilken, InfoZoom: Analysing Formula One racing results with an interactive data mining and visualisation tool. In Proceedings of 2nd International Conference on Data Mining, pages 455–464, Cambridge University, UK, 2000.
[23] M. Spenke, C. Beilken, and T. Berlage, FOCUS: the interactive table for product comparison and selection. In Proceedings of the 9th annual ACM symposium on User interface software and technology, pages 41–50, 1996.
[24] J. Stasko, C. Gärg, Z. Liu, and K. Singhal, Jigsaw: Supporting investigative analysis through interactive visualization. Information Visualization, 7 (2): 118–132, 2008.
[25] T. Takada and H. Koike, MieLog: A Highly Interactive Visual Log Browser Using Information Visualization and Statistical Analysis. In Proceedings of the 16th USENIX Conference on System Administration, pages 133–144, Philadelphia, PA, 2002.
[26] C. Tominski, J. Abello, and H. Schumann, Axes-based visualizations with radial layouts. In Proceedings of the 2004 ACM symposium on Applied computing, pages 1242–1247. ACM New York, NY, USA, 2004.
[27] M. Wattenberg, Visualizing the stock market. Conference on Human Factors in Computing Systems, pages 188–189, 1999.
[28] C. Weaver, D. Fyfe, A. Robinson, D. Holdsworth, D. Peuquet, and A. M. MacEachren, Visual exploration and analysis of historic hotel visits. Information Visualization, 6 (1): 89–103, 2007.
[29] K. Wittenburg, T. Lanning, M. Heinrichs, and M. Stanton, Parallel bar-grams for consumer-based information exploration and choice. In Proceedings of the 14th annual ACM symposium on User interface software and technology, pages 51–60, 2001.
[30] K. P. Yee, K. Swearingen, K. Li, and M. Hearst, Faceted metadata for image search and browsing. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 401–408, 2003.

Index Terms:
investigative analysis, transaction analysis, information visualization, multiple views, time series data, multiple attributes, categorical data
Citation:
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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