History Viewer: Displaying User Interaction History in Visual Analytics Applications

  • Vinícius C. V. B. SeguraEmail author
  • Simone D. J. Barbosa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9733)


Effective and efficient strategies are needed to extract unknown and unexpected information from data of unprecedentedly large size, high dimensionality, and complexity [7]. Only a combination of data analysis and visualization techniques can handle these complex and dynamic data [4]. Visual analytics applications aim to integrate the best of both sides.

After the knowledge discovery process, a major challenge is to filter the essential information that led to a discovery and to communicate the findings to other people. We propose taking advantage of the trace left by the exploratory data analysis, in the form of user interaction history. This paper presents a framework to instrument web visual analytics applications, logging the user interaction during the exploratory data analysis. This paper also presents our solution to display the user interaction history to the user, enabling him to revisit the steps that led to an insight.


Visual analytics User interaction logging User interaction history Data visualization History visualization Log visualization 



The authors would like to thank CNPq for the financial support to their work (processes #309828/2015-5 and #453996/2014-0).


  1. 1.
    Aigner, W., Miksch, S., Müller, W., Schumann, H., Tominski, C.: Visualizing time-oriented data - a systematic view. Comput. Graph. 31(3), 401–409 (2007). CrossRefGoogle Scholar
  2. 2.
    Brehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. IEEE Trans. Vis. Comput. Graph. 19(12), 2376–2385 (2013)CrossRefGoogle Scholar
  3. 3.
    Green, T., Ribarsky, W., Fisher, B.: Visual analytics for complex concepts using a human cognition model. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 91–98, October 2008Google Scholar
  4. 4.
    Keim, D.A., Mansmann, F., Oelke, D., Ziegler, H.: Visual analytics: combining automated discovery with interactive visualizations. In: Boulicaut, J.-F., Berthold, M.R., Horváth, T. (eds.) DS 2008. LNCS (LNAI), vol. 5255, pp. 2–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Key, A., Howe, B., Perry, D., Aragon, C.: Vizdeck: self-organizing dashboards for visual analytics. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, pp. 681–684. ACM, New York (2012)Google Scholar
  6. 6.
    Kohlhammer, J., Keim, D., Pohl, M., Santucci, G., Andrienko, G.: Solving problems with visual analytics. Procedia Comput. Sci. 7, 117–120 (2011). Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET11). CrossRefGoogle Scholar
  7. 7.
    Mennis, J., Guo, D.: Spatial data mining and geographic knowledge discovery-an introduction. Comput. Environ. Urban Syst. 33(6), 403–408 (2009). Spatial Data Mining–Methods and Applications. CrossRefGoogle Scholar
  8. 8.
    Oliveira, I., Segura, V., Nery, M., Mantripragada, K., Ramirez, J.P., Cerqueira, R.: WISE: A web environment for visualization and insights on weather data. In: WVIS - 5thWorkshop on Visual Analytics, Information Visualization and Scientific Visualization, SIBGRAPI 2014, pp. 4–7 (2014).
  9. 9.
    Souza, C.S.D., Prates, R.O., Carey, T.: Missing and declining affordances: are these appropriate concepts? J. Braz. Comput. Soc. 7, 26–34 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vinícius C. V. B. Segura
    • 1
    • 2
    Email author
  • Simone D. J. Barbosa
    • 1
  1. 1.Departamento de InformáticaPUC-RioRio de JaneiroBrazil
  2. 2.IBM ResearchRio de JaneiroBrazil

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