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Issue No.12 - Dec. (2012 vol.18)
pp: 2669-2678
Conglei Shi , Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Panpan Xu , Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Wei Chen , Zhe Jiang Univ., Lin'An, China
Huamin Qu , Hong Kong Univ. of Sci. & Technol., Hong Kong, China
ABSTRACT
For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.
INDEX TERMS
time series, data visualisation, search engines, interactive exploration, RankExplorer, ranking change visualization, time series data, search engine, Google, Bing, segmentation method, time series curve, ranking category, extended ThemeRiver view, embedded color bars, changing glyph, user interaction, Image color analysis, Time series analysis, Data visualization, Market research, Encoding, interaction techniques, Time-series data, ranking change, Themeriver
CITATION
Conglei Shi, Weiwei Cui, Shixia Liu, Panpan Xu, Wei Chen, Huamin Qu, "RankExplorer: Visualization of Ranking Changes in Large Time Series Data", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2669-2678, Dec. 2012, doi:10.1109/TVCG.2012.253
REFERENCES
[1] W. Aigner, S. Miksch, W. Müller., H. Schumann, and C. Tominski, Visual methods for analyzing time-oriented data IEEE Transactions on Visualization and Computer Graphics, 14(1): 47-60, 2008.
[2] L. Byron and M. Wattenberg, Stacked graphs—geometry & aesthetics IEEE Transactions on Visualization and Computer Graphics, 14(6): 1245-52, 2008.
[3] W. Cui, S. Liu, L. Tan., C. Shi, Y. Song,Z. J. Gao, X. Tong, and H. Qu, TextFlow: towards better understanding of evolving topics in text IEEE Transactions on Visualization and Computer Graphics, 17(12): 2412-21, 2011.
[4] C. Daassi, L. Nigay, and M. Fauvet, A taxonomy of temporal data visualization techniques information-interaction-intelligence, 5(2) pp. 41-63, 2005.
[5] M. Dörk,D. M. Gruen, C. Williamson, and M. S T. Carpendale., A visual backchannel for large-scale events IEEE Transactions on Visualization and Computer Graphics, 16(6): 1129-1138, 2010.
[6] G. Ellis and A. Dix, A taxonomy of clutter reduction for information visualisation IEEE Transactions on Visualization and Computer Graphics, 13(6): 1216-23, 2007.
[7] M. Hao, U. Dayal, D. Keirn,, and T. Schreck., Importance-driven visualization layouts for large time series data. In Proceedings of the IEEE Symposium on Information Visualization (Info Vis), pages 203-210. IEEE, 2005.
[8] M. Hao, U. Dayal, D. Keirn,, and T. Schreck., Multi-Resolution Techniques for Visual Exploration of Large Time-Series Data. Symposium A Quarterly Journal In Modern foreign literatures, pages 1-8, 2007.
[9] S. Havre, B. Hetzler, and L. Nowell, ThemeRiver: visualizing theme changes over time In Proceedings of the IEEE Symposium on Information Visualization (Info Vis), pages 115-123, 2000.
[10] T. H. Cormen,C. E. Leiserson,R. L. Rivest,, and C. Stein, Introduction to Algorithms. MIT Press and McGraw-Hill, 3rd edition, Feb. 2009.
[11] J. Heer, N. Kong, and M. Agrawala, Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations In Proceedings of the 27th International conference on Human factors in computing systems (CHI), pages 1303-1312, 2009.
[12] H. Hochheiser and B. Shneiderman, Dynamic query tools for time series data sets: Timebox widgets for interactive exploration Information Visualization, 3(1): 1-18, 2004.
[13] W. Javed and N. Elmqvist, Stack zooming for multi-focus interaction in time-series data visualization In IEEE Pacific Visualization Symposium (PacificVis), pages 33-40, Mar. 2010.
[14] W. Javed, B. McDonnel, and N. Elmqvist, Graphical perception of multiple time series IEEE Transactions on Visualization and Computer Graphics, 16(6): 927-34, 2010.
[15] D. Keirn, M. Ankerst, and H. Kriegel., Recursive pattern: A technique for visualizing very large amounts of data. In Proceedings of the 6th conference on Visualization’95, pages 279-286. IEEE Computer Society, 1995.
[16] D. Keirn, T. Nietzschmann, N. Schelwies., J. Schneidewind, T. Schreck,, and H. Ziegler., A Spectral Visualization System for Analyzing Financial Time Series Data. Time, 2006.
[17] P. Kidwell, G. Lebanon, and W. Cleveland, Visualizing incomplete and partially ranked data IEEE Transactions on Visualization and Computer Graphics, 14(6): 1356-1363, 2008.
[18] R. Kincaid, Signallens: Focus+ context applied to electronic time series IEEE Transactions on Visualization and Computer Graphics, 16(6): 900-907, 2010.
[19] M. Krstajic, E. Bertini, and D. Keirn, CloudLines: Compact Display of Event Episodes in Multiple Time-Series IEEE Transactions on Visualization and Computer Graphics, 17(12): 2432-2439, 2011.
[20] B. Lee, N. Riche, A. Karlson,, and S. Carpendale., Sparkclouds: Visualizing trends in tag clouds. IEEE Transactions on Visualization and Computer Graphics, 16(6): 1182-1189, 2010.
[21] S. Liu,M. X. Zhou, S. Pan, Y. Song., W. Qian, W. Cai,, and X. Lian., Tiara: Interactive, topic-based visual text summarization and analysis. ACM TIST, 3(2): 25, 2012.
[22] Z. Liu, J. Stasko, and T. Sullivan, Selltrend: Inter-attribute visual analysis of temporal transaction data IEEE Transactions on Visualization and Computer Graphics, 15(6): 1025-1032, 2009.
[23] P. Mclachlan, T. Munzner, E. Koutsofios,, and S. North., LiveRAC: interactive visual exploration of system management time-series data. In Proceeding of the 26th International conference on Human factors in computing systems (CHI), pages 1483-1492, 2008.
[24] W. Muller and H. Schumann., Visualization methods for time-dependent data-an overview. In Proceedings of the Winter Simulation Conference, 1, pages 737-745. IEEE, 2003.
[25] P. Mutzel and J. Michael, Simple and Efficient Bilayer Cross Counting Journal of Graph Algorithms and Applications, 8(2): 179-194, 2004.
[26] W. Playfair., The Commercial and Political Atlas and Statistical Breviary. New York: Cambridge Univeristy Press. (Original work published 1786), 2005.
[27] L. Shi, F. Wei, S. Liu., L. Tan, X. Lian,, and M. X. Zhou., Understanding text corpora with multiple facets. In IEEE Symposium on Visual Analytics Science and Technology. pages 99-106, 2010.
[28] B. Shneiderman., The eyes have it: A task by data type taxonomy for information visualizations. In VL, pages 336-343, 1996.
[29] S. Silva and T. Catarci., Visualization of linear time-oriented data: survey. In Web Information Systems Engineering, 2000. Proceedings c the First International Conference on, 1, pages 310-319. IEEE 2000.
[30] J. Van Wijk and E. Van Selow., Cluster and calendar based visualization c time series data. In Proceedings 1999 IEEE Symposium on Informatio Visualization (InfoVis‘99), pages 4-9,. IEEE Comput. Soc, 1999.
[31] C. Wang, H. Yu, and K. Ma, Importance-driven time-varying data visu alization IEEE Transactions on Visualization and Computer Graphic: 14(6): 1547-1554, 2008.
[32] T. D. Wang, C. Plaisant, B. Shneidcrrnan., N. Spring, D. Rosemar, G. Marchand, V. Mukherjee, and M. Smith., Temporal summarie: supporting temporal categorical searching, aggregation and compari son IEEE Transactions on Visualization and Computer Graphics 15(6): 1049-56, 2009.
[33] C. Ware., Information Visualization: Perception for Design (Interactiv Technologies). Morgan Kaufmann, 1 st edition, Feb. 2000.
[34] M. Wattenberg., Baby names, visualization, and social data analysis. I Proceedings of the IEEE Symposium on Information Visualization (Info Vis), pages 1-7, 2005.
[35] J. Woodring and H.-W. Shen., Multiscale time activity data exploratio via temporal clustering visualization spreadsheet IEEE Transactions 0 Visualization and Computer Graphics, 15(1): 123-37, 2009.
[36] J. Zhao, F. Chevalier, and R. Balakrishnan., Kronominer: using multi-foe navigation for the visual exploration of time-series data. In Proceeding of the 29th international conference on Human factors in computing systems (CHI), pages 1737-1746. ACM, 2011.
[37] J. Zhao, F. Chevalier, E. Pietriga,, and R. Balakrishnan., Exploratory analysis of time-series with chronolenses. IEEE Transactions on visualizatio and Computer Graphics, 17(12): 2422-2431, 2011.
[38] H. Ziegler, M. Jenny, T. Gruse,, and D. Keirn., Visual market sector anal)‘ sis for financial time series data. In IEEE Symposium on Visual Analytic Science and Technology, pages 83-90, 2010.
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