Visualization for the Masses: Learning from the Experts

  • Jill Freyne
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6176)


Increasingly, in our everyday lives, we rely on our ability to access and understand complex information. Just as the search engine played a key role in helping people access relevant information, there is evidence that the next generation of information tools will provide users with a greater ability to analyse and make sense of large amounts of raw data. Visualization technologies are set to play an important role in this regard. However, the current generation of visualization tools are simply too complex for the typical user. In this paper we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, ManyEyes, and explore how case-based representation of datasets including simple features such as size and content types can produce recommendations to assist novice users in the selection of appropriate visualization types.


Novice User Case Representation Recommendation List Recommendation Strategy Target Case 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    André, E., Rist, T.: The design of illustrated documents as a planning task. American Association for Artificial Intelligence, Menlo Park (1993)Google Scholar
  2. 2.
    Briggs, P., Smyth, B.: Provenance, trust, and sharing in peer-to-peer case- based web search. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 89–103. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Casner, S.M.: Task-analytic approach to the automated design of graphic presentations. ACM Trans. Graph. 10(2), 111–151 (1991)CrossRefGoogle Scholar
  4. 4.
    Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: IUI 2009: Proceedings of the 13th international conference on Intelligent user interfaces, pp. 315–324. ACM, New York (2009)Google Scholar
  5. 5.
    Leake, D.B., Whitehead, M.: Case provenance: The value of remembering case sources. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 194–208. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110–141 (1986)CrossRefGoogle Scholar
  7. 7.
    Mackinlay, J., Hanrahan, P., Stolte, C.: Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics 13(6), 1137 (2007)CrossRefGoogle Scholar
  8. 8.
    Roth, S.F., Kolojejchick, J., Mattis, J., Goldstein, J.: Interactive graphic design using automatic presentation knowledge. In: CHI 1994: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 112–117. ACM, New York (1994)Google Scholar
  9. 9.
    Tufte, E.R.: The visual display of quantitative information. American Journal of Physics 53, 1117 (1985)CrossRefGoogle Scholar
  10. 10.
    Tufte, E.R.: The cognitive style of PowerPoint. Graphics Press Cheshire, Conn. (2004)Google Scholar
  11. 11.
    Tufte, E.R.: Beautiful evidence. Graphics Press Cheshire, Conn. (2006)Google Scholar
  12. 12.
    Viégas, F.B., Wattenberg, M.: TIMELINES Tag clouds and the case for vernacular visualization. Interactions 15(4), 49–52 (2008)CrossRefGoogle Scholar
  13. 13.
    Viegas, F.B., Wattenberg, M., van Ham, F., Kriss, J., McKeon, M.: Manyeyes: A site for visualization at internet scale. IEEE Transactions on Visualization and Computer Graphics 13(6), 1121–1128 (2008)CrossRefGoogle Scholar
  14. 14.
    Zhou, M.X., Chen, M.: Automated generation of graphic sketches by example. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pp. 65–74. Morgan Kaufmann, San Francisco (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jill Freyne
    • 1
  • Barry Smyth
    • 2
  1. 1.Tasmanian ICT Center, CSIROTasmaniaAustralia
  2. 2.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinDublinIreland

Personalised recommendations