A Design Framework for Instrumenting Analytic Provenance for Problem-Solving Tasks

  • Lingxue Yang
  • Pierre Morizet-Mahoudeaux
  • Anne Guénand
  • Assia Mouloudi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 739)


In the context of analytics applications, the recall of interaction history often happens when users are identifying the root causes of a given problem based on a visual analytics task, which can be interrupted or suspended. The research of analytic provenance focuses on retrieving users’ interaction history, reinstating their reasoning process so that they can quickly resume an interrupted or suspended task. Although many visualization analytic tools are available, they lack extended capabilities for giving access to users’ interaction history in a natural coupling with their actions. We propose a design framework for instrumenting analytic provenance in a mode allowing users to “re-commit” to their tasks. We realize a first experiment to see how one’s history activities has an impact on the way he/she resolves the task. We investigate the interaction possibilities of two design approaches: the user interface (UI) design in which the history path is considered as “put down” in the environment; the user experience (UX) design considers it as a coupling device between the user and the world, being “in hand” mode. The first part of our analysis shows that users use the history path for supporting their reasoning process. However, the indirect coupling between users’ actions and provenance function keeps them outside of the history path so that they cannot easily link it to their current problem. We hypothesize that the “in hand” mode of interaction history will allow a natural coupling between a user’s action and the provenance function, which may lead to a positive user experience. We then propose the lines for designing dynamic history path interaction tools.


analytic provenance UX design design of a coupling device perception history path 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bavoil, L., Callahan, S. P., Crossno, P. J., Freire, J., Scheidegger, C. E., Silva, C. T., & Vo, H. T. VisTrails: Enabling interactive multiple-view visualizations. Proceedings of the IEEE Visualization Conference, (May 2014), 18. (2005).
  2. 2.
    Cowan, N.: What are the differences between long-term, short-term, and working memory? Nelson. NIH Public Access, 6123(7), 323–338. (2009).
  3. 3.
    Dunne, C., Henry Riche, N., Lee, B., Metoyer, R., & Robertson, G. GraphTrail: Analyzing Large Multivariate, Heterogeneous Networks while Supporting Exploration History. Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems - CHI ’12, 1663–1664. (2012).
  4. 4.
    Feng, M., Deng, C., Peck, E. M., & Harrison, L. HindSight: Encouraging Exploration through Direct Encoding of Personal Interaction History. IEEE Transactions on Visualization and Computer Graphics, 23(1), 351–360. (2017).
  5. 5.
    Heer, J., Mackinlay, J. D., Stolte, C., & Agrawala, M.: Graphical Histories for Visualization : Supporting Analysis, Communication, and Evaluation, 14(6), 1189–1196 (2008).Google Scholar
  6. 6.
    Kadivar, N., Chen, V., Dunsmuir, D., Lee, E., Qian, C., Dill, J., … Woodbury, R. CzSaw - Capturing and supporting the analysis process. VAST 09 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings, 131–138. (2009).
  7. 7.
    Lamming, M. G. ., & Newman, W. M.:Activity-based Information Retrieval : Technology in Support of Personal Memory. In Friedrich H. Vogt (Ed.), 12th World Computer Congress on Personal Computers and Intelligent Systems - Information Processing ’92 (pp. 68–81). Amsterdam: North-Holland Publishing Co. Amsterdam, The Netherlands, The Netherlands ©1992. (1992).Google Scholar
  8. 8.
    Lenay, C., Thouvenin, I., Guénand, A., Gapenne, O., Stewart, J., & Maillet, B.: Designing the ground for pleasurable experience. Proceedings of the 2007 Conference on Designing Pleasurable Products and Interfaces - DPPI ’07, (August), 35. (2007).
  9. 9.
    Nguyen, P. H., Xu, K., Bardill, A., Salman, B., Herd, K., & Wong, B. L. W. Sense Map: Supporting browser-based online sensemaking through analytic provenance. 2016 IEEE Conference on Visual Analytics Science and Technology, VAST 2016 - Proceedings, (October), 91–100. (2016).
  10. 10.
    North, C., Chang, R., Endert, A., Dou, W., May, R., Pike, B., & Fink, G. Analytic Provenance: Process+Interaction+Insight. CHI ’11 Extended Abstracts on Human Factors in Computing Systems, 33–36. (2011).
  11. 11.
    Pernice, K., & Nielsen, J.: How to Conduct Eyetracking Studies, (August), 159. Retrieved from (2009).
  12. 12.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. Proceedings 1996 IEEE Symposium on Visual Languages, 336–343. (1996).
  13. 13.
    Yang, L., Morizet-mahoudeaux, P., Guénand, A., & Mouloudi, A.: First steps towards the emergence of emotions in interaction design (p. 2016) (2016).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Sorbonne-Universités, Université de Technologie de Compiègne, UMR CNRS 7253 HEUDIASYCCompiègne-CedexFrance
  2. 2.Sorbonne-Universités, Université de Technologie de Compiègne Laboratoire COSTECHCompiègne-CedexFrance
  3. 3.3SAPParisFrance

Personalised recommendations