From analytical purposes to data visualizations: a decision process guided by a conceptual framework and eye tracking

  • Jens GuldenEmail author
  • Andrea Burattin
  • Amine A. Andaloussi
  • Barbara Weber
Special Section Paper


Data visualizations are versatile tools for gaining cognitive access to large amounts of data and for making complex relationships in data understandable. This paper proposes a method for assessing data visualizations according to the purposes they fulfill in domain-specific data analysis settings. We introduce a framework that gets configured for a given analysis domain and allows to choose data visualizations in a methodically justified way, based on analysis questions that address different aspects of data to be analyzed. Based on the concepts addressed by the analysis questions, the framework provides systematic guidance for determining which data visualizations are able to serve which conceptual analysis interests. In a second step of the method, we propose to follow a data-driven approach and to experimentally compare alternative data visualizations for a particular analytical purpose. More specifically, we propose to use eye tracking to support justified decisions about which of the data visualizations selected with the help of the framework are most suitable for assessing the analysis domain in a cognitively efficient way. We demonstrate our approach of how to come from analytical purposes to data visualizations using the example domain of Process Modeling Behavior Analysis. The analyses are performed on the background of representative analysis questions from this domain.


Data visualization Process execution data Process Modeling Behavior Analysis Eye tracking Reading patterns Process mining 



This work is partially funded by the Austrian Science Fund Project “The Modeling Mind: Behavior Patterns in Process Modeling” (P26609).


  1. 1.
    van der Aalst, W.M.: Process Mining, 2nd edn. Springer, Berlin (2016). CrossRefGoogle Scholar
  2. 2.
    Basili, V.R., Selby, R.W., Hutchens, D.H.: Experimentation in software engineering. IEEE Trans. Softw. Eng. 12(7), 733–743 (1986).
  3. 3.
    Bennett, K.B., Flach, J.M.: Display and Interface Design—Subtle Science. Exact Art. CRC Press, Boca Raton (2011)CrossRefGoogle Scholar
  4. 4.
    Berson, A., Smith, S.J.: Data Warehousing, Data Mining, & OLAP. McGraw-Hill, New York (2004)Google Scholar
  5. 5.
    Bertin, J.: Semiology of Graphics: Diagrams, Networks. Maps. University of Wisconsin Press, Madison (1983)Google Scholar
  6. 6.
    Borner, K., Polley, D.E.: Visual Insights: A Practical Guide to Making Sense of Data. MIT Press, Cambridge (2014)Google Scholar
  7. 7.
    Buckl, S., Ernst, A., Matthes, F., Schweda, C.: Enterprise architecture management patterns for enterprise architecture visioning. In: Proceedings of the 14th Annual European Conference on Pattern Languages of Programming (EuroPLoP 2009). Irsee, Germany (2009)Google Scholar
  8. 8.
    Burattin, A., Kaiser, M., Neurauter, M., Weber, B.: Eye tracking meets the process of process modeling: a visual analytic approach. In: TAProViz (2016)Google Scholar
  9. 9.
    Cairo, A.: The Functional Art. Voices that Matter. Pearson Education (2010)Google Scholar
  10. 10.
    Chen, C.: Information Visualization. Springer, London (2010)Google Scholar
  11. 11.
    Chen, C., Härdle, W.K., Unwin, A. (eds.): Handbook of Data Visualization. Springer, Berlin (2008)zbMATHGoogle Scholar
  12. 12.
    Claes, J., Vanderfeesten, I., Pinggera, J., Reijers, H.A., Weber, B., Poels, G.: A visual analysis of the process of process modeling. IseB 13(1), 147–190 (2015). CrossRefGoogle Scholar
  13. 13.
    Claes, J., Vanderfeesten, I.T.P., Gailly, F., Grefen, P., Poels, G.: The structured process modeling method (SPMM) what is the best way for me to construct a process model? Decis. Support Syst. 100, 57–76 (2017). CrossRefGoogle Scholar
  14. 14.
    Codd E.F., C.S.B.S.C.T.: Providing olap (on-line analytical processing) to user analysts: an it mandate (1993)Google Scholar
  15. 15.
    Cooper, A.: About Face: The Essentials of Interaction Design, 4th edn. Wiley, Hoboken (2014)Google Scholar
  16. 16.
    Few, S.: Information Dashboard Design: The Effective Visual Communication of Data. O’Reilly, Sebastopol (2006)Google Scholar
  17. 17.
    Frank, U., Heise, D., Kattenstroth, H.: Use of a domain specific modeling language for realizing versatile dashboards. In: J.P. Tolvanen, M. Rossi, J. Gray, J. Sprinkle (eds.) Proceedings of the 9th OOPSLA Workshop on Domain-Specific Modeling (DSM), Helsinki Business School, Helsinki, 2009 (2009)Google Scholar
  18. 18.
    Gärdenfors, P.: Conceptual Spaces. MIT Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  19. 19.
    Goldberg, J.H., Kotval, X.P.: Computer interface evaluation using eye movements: methods and constructs. Int. J. Ind. Ergon. 24(6), 631–645 (1999).
  20. 20.
    Gulden, J.: Visually comparing process dynamics with rhythm-eye views. In: Dumas, M., Fantinato, M. (eds.) Business Process Management Workshops: BPM 2016 International Workshops, Rio de Janeiro, Brazil, September 19, 2016. Revised Papers, Springer, Berlin (2016)Google Scholar
  21. 21.
    Gulden, J., van der Linden, D., Aysolmaz, B.: Requirements for research on visualizations in information systems engineering. In: ENASE Conference (2016)Google Scholar
  22. 22.
    Gulden, J., Reijers, H.A.: Toward advanced visualization techniques for conceptual modeling. In: CAiSE Forum. CEUR (2015)Google Scholar
  23. 23.
    Hichert, R., Faisst, J., et al.: International business communication standards (ibcs\(\textregistered \)) v. 1.1 (2017).
  24. 24.
    Holmqvist, K.: Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press, Oxford (2011)Google Scholar
  25. 25.
    IEEE Task Force on Process Mining: Process Mining Manifesto. In: F. Daniel, K. Barkaoui, S. Dustdar (eds.) Business Process Management Workshops, pp. 169–194. Springer-Verlag (2011)Google Scholar
  26. 26.
    Johnson, M., Lakoff, G.: Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. Basic Books, New York (1999)Google Scholar
  27. 27.
    Juristo, N., Moreno, A.M.: Basics of Software Engineering Experimentation (2001)Google Scholar
  28. 28.
    Khosroshahi, P.A., Hauder, M., Schneider, A.W., Matthes, P.D.F.: Enterprise architecture management pattern catalog version 2.0. Tech. rep., Technical University Munich, Munich (2015)Google Scholar
  29. 29.
    Kirk, A.: Data Visualization: a successful design process. Packt Publishing (2012)Google Scholar
  30. 30.
    Kleiner, N.: Can business process changes be cheaper implemented with workflow management systems? Innovations Through Information Technology 1 and 2, 529–532 (2004)Google Scholar
  31. 31.
    Kurzhals, K., Fisher, B.D., Burch, M., Weiskopf, D.: Eye tracking evaluation of visual analytics. Information Visualization 15(4), 340–358 (2016). CrossRefGoogle Scholar
  32. 32.
    Pinggera, J.: The Process of Process Modeling. Ph.D. thesis, University of Innsbruck, Department of Computer Science (2014)Google Scholar
  33. 33.
    Pinggera, J., Soffer, P., Fahland, D., Weidlich, M., Zugal, S., Weber, B., Reijers, H., Mendling, J.: Styles in business process modeling: an exploration and a model. Softw. Syst. Modeling 14(3), 1055–1080 (2015). CrossRefGoogle Scholar
  34. 34.
    Pinggera, J., Soffer, P., Fahland, D., Weidlich, M., Zugal, S., Weber, B., Reijers, H.A., Mendling, J.: Styles in business process modeling: an exploration and a model. Softw. Syst. Modeling 14(3), 1055–1080 (2015). CrossRefGoogle Scholar
  35. 35.
    Pinggera, J., Zugal, S., Weidlich, M., Fahland, D., Weber, B., Mendling, J., Reijers, H.: Tracing the process of process modeling with modeling phase diagrams. In: Proc. ER-BPM ’11, pp. 370–382 (2012)Google Scholar
  36. 36.
    Poole, A., Ball, L.J.: Eye tracking in human-computer interaction and usability research: current status and future. In: Prospects”, Chapter in C. Ghaoui (Ed.): Encyclopedia of Human-Computer Interaction. Pennsylvania: Idea Group, Inc (2005)Google Scholar
  37. 37.
    Poole, A., Ball, L.J., Phillips, P.: In Search of Salience: A Response-time and Eye-movement Analysis of Bookmark Recognition, pp. 363–378. Springer London, London (2005).
  38. 38.
    Sharp, H.: Interaction Design. Wiley, Hoboken (2011)Google Scholar
  39. 39.
    Shmueli, G., Bruce, P., Yahav, I., Patel, N., Lichtendahl, K.: Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. Wiley (2017).
  40. 40.
    Sjoeberg, D.I.K., Hannay, J.E., Hansen, O., Kampenes, V.B., Karahasanovic, A., Liborg, N., Rekdal, A.C.: A survey of controlled experiments in software engineering. IEEE Trans. Softw. Eng. 31(9), 733–753 (2005). CrossRefGoogle Scholar
  41. 41.
    Spence, R.: Information Visualization. Prentice Hall, Upper Saddle River (2007)Google Scholar
  42. 42.
    Surma, J.: Business Intelligence: Making Decisions Through Data Analytics. Business Expert Press, New York (2011)CrossRefGoogle Scholar
  43. 43.
    Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1983)Google Scholar
  44. 44.
    Weber, B., Gulden, J., Burattin, A.: Designing visual decision making support with the help of eye-tracking. In: S. Nurcan, J. Gulden (eds.) BPMDS 2017 Radar Proceedings. CEUR (2017)Google Scholar
  45. 45.
    Zelkowitz, M.V., Wallace, D.R.: Experimental models for validating technology. Computer 31(5), 23–31 (1998). CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jens Gulden
    • 1
    Email author
  • Andrea Burattin
    • 2
  • Amine A. Andaloussi
    • 2
  • Barbara Weber
    • 2
    • 3
  1. 1.University of Duisburg-EssenEssenGermany
  2. 2.Technical University of DenmarkKongens LyngbyDenmark
  3. 3.University of St. GallenSt. GallenSwitzerland

Personalised recommendations