The Community for Technology Leaders
RSS Icon
Issue No.01 - January/February (2008 vol.14)
pp: 47-60
Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis.We will discuss three major aspects — visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations.For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis.We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.
Time-Oriented Data, Visualization, Analysis, User
Wolfgang Aigner, Silvia Miksch, Wolfgang Müller, Heidrun Schumann, Christian Tominski, "Visual Methods for Analyzing Time-Oriented Data", IEEE Transactions on Visualization & Computer Graphics, vol.14, no. 1, pp. 47-60, January/February 2008, doi:10.1109/TVCG.2007.70415
[1] B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” Proc. IEEE Symp. Visual Languages, pp. 336-343, 1996.
[2] J.J. Thomas and K.A. Cook, “A Visual Analytics Agenda,” IEEE Computer Graphics and Applications, vol. 26, no. 1, pp. 10-13, 2006.
[3] E. Hajnicz, Time Structures: Formal Description and Algorithmic Representation. Springer, 1996.
[4] A.U. Frank, “Different Types of ‘Times’ in GIS,” Spatial and Temporal Reasoning in Geographic Information Systems, M.J. Egenhofer and R.G. Golledge, eds., Oxford Univ. Press, 1998.
[5] W. Aigner, “Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design,” PhD dissertation, Vienna Univ. of Tech nology, 2006.
[6] I.A. Goralwalla, M.T. Özsu, and D. Szafron, “An Object-Oriented Framework for Temporal Data Models,” Temporal Databases: Research and Practice, O. Etzion, S. Jajodia, and S.M. Sripada, eds., pp. 1-35. Springer, 1998.
[7] W. Müller and H. Schumann, “Visualization Methods for Time-Dependent Data—An Overview,” Proc. Winter Simulation 2003, Dec. 2003.
[8] S.F. Silva and T. Catarci, “Visualization of Linear Time-Oriented Data: A Survey (extended version),” J. Applied System Studies, vol. 3, no. 2, 2002.
[9] M. Weber, M. Alexa, and W. Müller, “Visualizing Time-Series on Spirals,” Proc. IEEE Symp. Information Visualization (InfoVis '01), pp. 7-14, Oct. 2001.
[10] J.V. Carlis and J.A. Konstan, “Interactive Visualization of Serial Periodic Data,” Proc. Symp. User Interface Software and Technology (UIST '98), 1998.
[11] K.P. Hewagamage, M. Hirakawa, and T. Ichikawa, “Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map,” Proc. Symp. Visual Languages (VL '99), 1999.
[12] C. Tominski, J. Abello, and H. Schumann, “Axes-Based Visualizations with Radial Layouts,” Proc. ACM Symp. Applied Computing, pp. 1242-1247, 2004.
[13] C. Tominski, J. Abello, and H. Schumann, “Interactive Poster: 3D Axes-Based Visualizations for Time Series Data,” Poster Compendium of IEEE Symp. Information Visualization (InfoVis '05), 2005.
[14] W. Aigner, S. Miksch, B. Thurnher, and S. Biffl, “PlanningLines: Novel Glyphs for Representing Temporal Uncertainties and Their Evaluation,” Proc. Ninth Int'l Conf. Information Visualisation (IV'05), 2005.
[15] C. Plaisant, B. Milash, A. Rose, S. Widoff, and B. Shneiderman, “LifeLines: Visualizing Personal Histories,” Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI '96), 1996.
[16] L. Chittaro and C. Combi, “Visualizing Queries on Databases of Temporal Histories: New Metaphors and Their Evaluation,” Data and Knowledge Eng., vol. 44, no. 2, pp. 239-264, 2003.
[17] S. Havre, E. Hetzler, P. Whitney, and L. Nowell, “ThemeRiver: Visualizing Thematic Changes in Large Document Collections,” IEEE Trans. Visualization and Computer Graphics, vol. 8, no. 1, pp. 9-20, Jan.-Mar. 2002.
[18] R.L. Harris, Information Graphics: A Comprehensive Illustrated Reference. Oxford Univ. Press, 1999.
[19] H. Hochheiser, “Interactive Graphical Querying of Time Series and Linear Sequence Data Sets,” PhD dissertation, Univ. of Maryland, 2003.
[20] H. Doleisch, H. Hauser, M. Gasser, and R. Kosara, “Interactive Focus+Context Analysis of Large, Time-Dependent Flow Simulation Data,” Trans. Soc. for Modeling and Simulation Int'l, 2007.
[21] J. Lin, E. Keogh, and S. Lonardi, “Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases,” Information Visualization, vol. 4, no. 2, pp. 61-82, 2005.
[22] D. Keim, “Scaling Visual Analytics to Very Large Data Sets,” Proc. Workshop Visual Analytics, June 2005.
[23] W.J. Clancey, “Heuristic Classification,” Artificial Intelligence, vol. 27, pp. 289-350, 1985.
[24] J.J. Thomas and K.A. Cook, Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Press, 2005.
[25] J. Lin, E. Keogh, S. Lonardi, and B. Chiu, “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms,” Proc. ACM Sigmod Workshop Research Issues in Data Mining and Knowledge Discovery, 2003.
[26] S. Miksch, W. Horn, C. Popow, and F. Paky, “Utilizing Temporal Data Abstraction for Data Validation and Therapy Planning for Artificially Ventilated Newborn Infants,” Artificial Intelligence in Medicine, vol. 8, no. 6, pp. 543-576, 1996.
[27] S. Miksch, A. Seyfang, W. Horn, and C. Popow, “Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data,” Proc. Joint European Conf. Artificial Intelligence in Medicine and Medical Decision Making (AIMDM '99), pp. 281-290, 1999.
[28] R. Bade, S. Schlechtweg, and S. Miksch, “Connecting Time-Oriented Data and Information to a Coherent Interactive Visualization,” Proc. 2004 Conf. Human Factors in Computing Systems (CHI '04), pp. 105-112, 2004.
[29] J. Lin, E. Keogh, L. Wei, and S. Lonardi, “Experiencing SAX: A Novel Symbolic Representation of Time Series,” Data Mining and Knowledge Discovery, vol. 15, no. 2, pp. 107-144, Oct. 2007.
[30] I.T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, second ed. Springer, 2002.
[31] S. dos Santos and K. Brodlie, “Gaining Understanding of Multivariate and Multidimensional Data through Visualization,” Computers and Graphics, vol. 28, pp. 311-325, 2004.
[32] S. Havre, E. Hetzler, and L. Nowell, “ThemeRiver: Visualizing Theme Changes over Time,” Proc. IEEE Symp. Information Visualization (InfoVis '00), pp. 115-123, Oct. 2000.
[33] T. Nocke, H. Schumann, and U. Böhm, “Methods for the Visualization of Clustered Climate Data,” Computational Statistics, vol. 19, no. 1, pp. 75-94, 2004.
[34] W. Müller, T. Nocke, and H. Schumann, “Enhancing the Visualization Process with Principal Component Analysis to Support the Exploration of Trends,” Proc. Asia Pacific Symp. Information Visualization (APVIS '06), 2006.
[35] A.K. Jain, M.N. Murty, and P.J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
[36] J.J. van Wijk and E.R. van Selow, “Cluster and Calendar Based Visualization of Time Series Data,” Proc. IEEE Symp. Information Visualization (InfoVis '99), pp. 4-9, 1999.
[37] T. Nocke, H. Schumann, U. Böhm, and M. Flechsig, “Information Visualization Supporting Modeling and Evaluation Tasks for Climate Models,” Proc. Winter Simulation 2003, Dec. 2003.
[38] J. Seo and B. Shneiderman, “A Rank-by-Feature Framework for Interactive Exploration of Multidimensional Data,” Information Visualization, vol. 4, no. 2, pp. 99-113, 2005.
[39] E. Keogh, H. Hochheiser, and B. Shneiderman, “An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data,” Proc. Fifth Int'l Conf. Flexible Query Answering Systems, 2002.
[40] K. Henriksen, J. Sporring, and K. Hornbaek, “Virtual Trackballs Revisited,” IEEE Trans. Visualization and Computer Graphics, vol. 10, no. 2, pp. 206-216, Apr.-June 2004.
[41] C. Tominski, “Event-Based Visualization for User-Centered Visual Analysis,” PhD dissertation, Univ. of Rostock, 2006.
[42] S. dos Santos and K. Brodlie, “Gaining Understanding of Multivariate and Multidimensional Data through Visualization,” Computers and Graphics, vol. 28, no. 3, pp. 311-325, 2004.
[43] R. Sadri, C. Zaniolo, A. Zarkesh, and J. Adibi, “Expressing and Optimizing Sequence Queries in Database Systems,” ACM Trans. Database Systems, vol. 29, no. 2, pp. 282-318, 2004.
[44] D.H. House, A.S. Bair, and C. Ware, “An Approach to the Perceptual Optimization of Complex Visualizations,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 4, pp. 509-521, July-Aug. 2006.
14 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool