Visualizing Functional Data with an Application to eBay’s Online Auctions

  • Wolfgang Jank
  • Galit Shmueli
  • Catherine Plaisant
  • Ben Shneiderman
Part of the Springer Handbooks Comp.Statistics book series (SHCS)


Technological advances in the measurement, collection, and storage of data have led tomore andmore complex data structures. Examples of such structures include measurements of the behavior of individuals over time, digitized two- or three-dimensional images of the brain, and recordings of three- or even four-dimensional movements of objects traveling through space and time. Such data, although recorded in a discrete fashion, are usually thought of as continuous objects that are represented by functional relationships. This gave rise to functional data analysis (FDA), which was made popular by the monographs of Ramsay and Silverman (1997, 2002), where the center of interest is a set of curves, shapes, objects, or, more generally, a set of functional observations, in contrast to classical statistics where interest centers on a set of data vectors. In that sense, functional data is not only different from the data structure studied in classical statistics, but it actually generalizes it. Many of these new data structures require new statistical methods to unveil the information that they carry.


Functional Data Auction Price Online Auction Information Visualization Functional Data Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Aris, A., Shneiderman, B., Plaisant, C., Shmueli, G. and Jank, W. (2005). Representing unevenly-spaced time series data for visualization and interactive exploration. In: International Conference on Human Computer Interaction (INTERACT 2005), 12–16 Sept 2005, Rome, Italy.Google Scholar
  2. Card, S., Mackinlay, J. and Shneiderman, B. (1999). Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, San Francisco, CA.Google Scholar
  3. Chen, C. (2004). Information Visualization: Beyond the Horizon. Springer, Berlin.Google Scholar
  4. Cleveland, W.S., Shyu, M. and Becker, R. (1996). The visual design and control of trellis display. Journal of Computational and Graphical Statistics, 5:123–155.CrossRefGoogle Scholar
  5. Hyde, V., Jank, W. and Shmueli, G. (2005). Investigating concurrency in online auctions through visualization. The American Statistician, 34(3):241–250.MathSciNetGoogle Scholar
  6. Jank, W. and Shmueli, G. (2005). Profiling price dynamics in online auctions using curve clustering. Technical report, Smith School of Business, University of Maryland, College Park, MD.Google Scholar
  7. Mills, K., Norminton, T. and Mills, S. (2005). Visualization of network scanning (poster presentation). In: National Defense and Homeland Security Kickoff Workshop of the Statistical and Applied Mathematical Sciences Institute (SAMSI), 11–15 Sept 2005, Research Triangle Park, NC.Google Scholar
  8. Plaisant, C. (2005). Information Visualization and the Challenge of Universal Access. In: Exploring Geovisualization. Elsevier, Oxford.Google Scholar
  9. Ramsay, J.O. and Silverman, B.W. (2005). Functional data analysis, 2nd edn. Springer, New York.Google Scholar
  10. Ramsay, J.O. and Silverman, B.W. (2002). Applied functional data analysis: methods and case studies. Springer, New York.zbMATHGoogle Scholar
  11. Ruppert, D., Wand, M.P. and Carroll, R.J. (2003). Semiparametric Regression. Cambridge University Press, Cambridge.zbMATHGoogle Scholar
  12. Schwalbe, D. (1996). VisualDSolve: Visualizing Differential Equations with Mathematica. TELOS/Springer, Berlin.Google Scholar
  13. Seo, J. and Shneiderman, B. (2005). A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4:99–113.CrossRefGoogle Scholar
  14. Shmueli, G. and Jank, W. (2005). Visualizing online auctions. Journal of Computational and Graphical Statistics, 14(2):299–319.CrossRefMathSciNetGoogle Scholar
  15. Shmueli, G., Jank, W., Aris, A., Plaisant, C. and Shneiderman, B. (2006). Exploring auction databases through interactive visualization. Decision Support Systems, 42(3):1521–1538.CrossRefGoogle Scholar
  16. Shneiderman, B. (2002). Inventing discovery tools: Combining information visualization with data mining. Information Visualization, 1:5–12.CrossRefGoogle Scholar
  17. Shneiderman, B. and Plaisant, C. (2004). Designing the User Interface: Strategies for Effective Human-Computer Interaction, 4th edn. Addison-Wesley, Reading, MA.Google Scholar
  18. van Wijk, J.J. and van Selow, E. (1999). Cluster and calendar-based visualization of time series data. In Wills, G. and Keim, D. (eds), Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis’99). IEEE, Los Alamitos, CA, pp. 4–9.Google Scholar
  19. Wang, S., Jank, W. and Shmueli, G. (2007). Explaining and Forecasting Online Auction Prices and their Dynamics using Functional Data Analysis. Forthcoming at the Journal of Business and Economic Statistics.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wolfgang Jank
    • 1
  • Galit Shmueli
    • 1
  • Catherine Plaisant
    • 1
  • Ben Shneiderman
    • 1
  1. 1.Department of Decision and Information TechnologiesUniversity of MarylandMarylandUSA

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