Analytic Trails: Supporting Provenance, Collaboration, and Reuse for Visual Data Analysis by Business Users

  • Jie Lu
  • Zhen Wen
  • Shimei Pan
  • Jennifer Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6949)


In this paper, we discuss the use of analytic trails to support the needs of business users when conducting visual data analysis, focusing particularly on the aspects of analytic provenance, asynchronous collaboration, and reuse of analyses. We present a prototype implementation of analytic trail technology as part of Smarter Decisions ( a web-based visual analytic tool, with the goal of helping business users derive insights from structured and unstructured data. To understand the value and shortcomings of trails in supporting visual analytic tasks in business environments, we performed a user study with 21 participants. While the majority of participants found trails to be useful for capturing and understanding the provenance of an analysis, they viewed trails as more valuable for personal use rather than for communicating the analytic process to other people as part of a collaboration. Study results also indicate that rich search mechanisms for easily finding relevant trails (or portions of a trail) is critical to the successful adaptation and reuse of existing saved trails.


Information visualization Visual data analysis Analytic provenance Asynchronous collaboration Analysis reuse 


  1. 1.
    Bavoil, L., Callahan, S., Crossno, P., Freire, J., Scheidegger, C., Silva, C., Vo, H.: Vistrails: Enabling interactive multiple-view visualizations. In: IEEE Vis (2005)Google Scholar
  2. 2.
    Brodlie, K., Brankin, L., Banecki, G., Gay, A., Poon, A., Wright, H.: Grasparc – a problem solving environment integrating computation and visualization. In: IEEE Vis (1993)Google Scholar
  3. 3.
    Chinchor, N., Pike, W.: Science of analytical reporting. Information Visualization 8, 286–293 (2009)CrossRefGoogle Scholar
  4. 4.
    Danis, C., Viegas, F., Wattenberg, M., Kriss, J.: Your place or mine? visualization as a community component. In: CHI (2008)Google Scholar
  5. 5.
    Derthick, M., Roth, S.: Example based generation of custom data analysis appliances. In: IUI (2001)Google Scholar
  6. 6.
    Fekete, J.-D., Grinstein, G., Plaisant, C.: IEEE InfoVis 2004 contest data set (2004),
  7. 7.
    Goodell, H., Chiang, C., Kelleher, C., Baumann, A., Grinstein, G.: Collecting and harnessing rich session histories. In: IV (2006)Google Scholar
  8. 8.
    Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: IUI (2009)Google Scholar
  9. 9.
    Gotz, D., Wen, Z., Lu, J., Kissa, P., Zhou, M., Cao, N., Qian, W., Liu, S.: HARVEST – Visualization and analysis for the masses. In: IEEE InfoVis Poster (2008)Google Scholar
  10. 10.
    Gotz, D., Zhou, M.: Characterizing users’ visual analytic activity for insight provenance. In: IEEE VAST (2008)Google Scholar
  11. 11.
    Groth, D., Streefkerk, K.: Provenance and annotation for visual exploration systems. IEEE Trans on Vis. and Comp. Graphics 12(6) (2006)Google Scholar
  12. 12.
    Heer, J., Agrawala, M.: Design considerations for collaborative visual analytics. Information 7(1), 49–62 (2008)Google Scholar
  13. 13.
    Heer, J., Mackinlay, J., Stolte, C., Agrawala, M.: Graphical histories for visualization: Supporting analysis, communication, and evaluation. IEEE Trans. on Vis. and Comp. Graphics 14(6), 1189–1196 (2008)CrossRefGoogle Scholar
  14. 14.
    Heer, J., Viegas, F., Wattenberg, M.: Voyagers and voyeurs: Supporting asynchronous collaborative information visualization. In: CHI (2007)Google Scholar
  15. 15.
    Jankun-Kelly, T., Kreylos, O., Ma, K., Hamann, B., Joy, K., Shalf, J., Bethel, E.: Deploying web-based visual exploration tools on the grid. IEEE Computer Graphics and Applications 23(2), 40–50 (2003)CrossRefGoogle Scholar
  16. 16.
    Jankun-Kelly, T., Ma, K., Gertz, M.: A model for the visualization exploration process. In: IEEE Vis (2002)Google Scholar
  17. 17.
    Kadivar, N., Chen, V., Dunsmuir, D., Lee, E., Qian, C., Dill, J., Shaw, C., Woodbury, R.: Capturing and supporting the analysis process. In: IEEE VAST (2009)Google Scholar
  18. 18.
    Keim, D., Mansmann, F., Schneidewind, J., Ziegler, H.: Challenges in visual data analysis. In: IEEE InfoVis (2006)Google Scholar
  19. 19.
    Kobsa, A.: An empirical comparison of three commercial information visualization systems. In: IEEE InfoVis (2001)Google Scholar
  20. 20.
    Kreuseler, M., Nocke, T., Schumann, H.: A history mechanism for visual data mining. In: IEEE InfoVis (2004)Google Scholar
  21. 21.
    Kurlander, D., Feiner, S.: Editable graphical histories. In: IEEE Workshop on Visual Language, pp. 127–134 (1998)Google Scholar
  22. 22.
    Lissack, M.: Of chaos and complexity: Managerial insights from a new science. Management Decision 35, 205–218 (1997)CrossRefGoogle Scholar
  23. 23.
    Ma, K.: Image graphs – a novel approach to visual data exploration. In: IEEE Vis (1999)Google Scholar
  24. 24.
    Shrinivasan, Y., van Wijk, J.: Supporting the analytical reasoning process in information visualization. In: CHI (2008)Google Scholar
  25. 25.
    Thomas, J., Cook, K. (eds.): Illuminating the path: The research and development agenda for visual analytics. IEEE Press, Los Alamitos (2005)Google Scholar
  26. 26.
    Wong, P., Thomas, J.: Visual analytics. IEEE Computer Graphics and Applications 24, 20–21 (2004)CrossRefGoogle Scholar
  27. 27.
    Xiao, L., Gerth, J., Hanrahan, P.: Enhancing visual analysis of network traffic using a knowledge representation. In: IEEE VAST (2007)Google Scholar
  28. 28.
    Yi, J., Kang, Y., Stasko, J., Jacko, J.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans on Vis. and Comp. Graphics 13(6) (2007)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Jie Lu
    • 1
  • Zhen Wen
    • 1
  • Shimei Pan
    • 1
  • Jennifer Lai
    • 1
  1. 1.IBM T. J. Watson Research CenterHawthorneUSA

Personalised recommendations