Skip to main content
SpringerLink
Log in

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

  • Special Section Paper
  • Published:
Software and Systems Modeling Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Larger versions of the figures are available at https://doi.org/10.5281/zenodo.1419594.

  2. All presented stimuli are available at https://doi.org/10.5281/zenodo.1419598.

  3. All data about participants’ background, expected tasks’ answers, and answers accuracy are available at https://doi.org/10.5281/zenodo.1419598.

  4. The complete source code for converting eye tracking data into event logs is available at https://github.com/DTU-SE/tsv2xes.

  5. See http://www.fluxicon.com/disco/.

References

  1. van der Aalst, W.M.: Process Mining, 2nd edn. Springer, Berlin (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. Basili, V.R., Selby, R.W., Hutchens, D.H.: Experimentation in software engineering. IEEE Trans. Softw. Eng. 12(7), 733–743 (1986). http://dl.acm.org/citation.cfm?id=9775.9777

  3. Bennett, K.B., Flach, J.M.: Display and Interface Design—Subtle Science. Exact Art. CRC Press, Boca Raton (2011)

    Book  Google Scholar 

  4. Berson, A., Smith, S.J.: Data Warehousing, Data Mining, & OLAP. McGraw-Hill, New York (2004)

    Google Scholar 

  5. Bertin, J.: Semiology of Graphics: Diagrams, Networks. Maps. University of Wisconsin Press, Madison (1983)

    Google Scholar 

  6. Borner, K., Polley, D.E.: Visual Insights: A Practical Guide to Making Sense of Data. MIT Press, Cambridge (2014)

    Google Scholar 

  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)

  8. Burattin, A., Kaiser, M., Neurauter, M., Weber, B.: Eye tracking meets the process of process modeling: a visual analytic approach. In: TAProViz (2016)

  9. Cairo, A.: The Functional Art. Voices that Matter. Pearson Education (2010)

  10. Chen, C.: Information Visualization. Springer, London (2010)

    Google Scholar 

  11. Chen, C., Härdle, W.K., Unwin, A. (eds.): Handbook of Data Visualization. Springer, Berlin (2008)

    MATH  Google Scholar 

  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). https://doi.org/10.1007/s10257-014-0245-4

    Article  Google Scholar 

  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). https://doi.org/10.1016/j.dss.2017.02.004

    Article  Google Scholar 

  14. Codd E.F., C.S.B.S.C.T.: Providing olap (on-line analytical processing) to user analysts: an it mandate (1993)

  15. Cooper, A.: About Face: The Essentials of Interaction Design, 4th edn. Wiley, Hoboken (2014)

    Google Scholar 

  16. Few, S.: Information Dashboard Design: The Effective Visual Communication of Data. O’Reilly, Sebastopol (2006)

    Google Scholar 

  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)

  18. Gärdenfors, P.: Conceptual Spaces. MIT Press, Cambridge (2000)

    Book  Google Scholar 

  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). https://doi.org/10.1016/S0169-8141(98)00068-7. http://www.sciencedirect.com/science/article/pii/S0169814198000687

  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. Gulden, J., van der Linden, D., Aysolmaz, B.: Requirements for research on visualizations in information systems engineering. In: ENASE Conference (2016)

  22. Gulden, J., Reijers, H.A.: Toward advanced visualization techniques for conceptual modeling. In: CAiSE Forum. CEUR (2015)

  23. Hichert, R., Faisst, J., et al.: International business communication standards (ibcs\(\textregistered \)) v. 1.1 (2017). http://www.ibcs-a.org/

  24. Holmqvist, K.: Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press, Oxford (2011)

    Google Scholar 

  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)

  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. Juristo, N., Moreno, A.M.: Basics of Software Engineering Experimentation (2001)

  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)

  29. Kirk, A.: Data Visualization: a successful design process. Packt Publishing (2012)

  30. Kleiner, N.: Can business process changes be cheaper implemented with workflow management systems? Innovations Through Information Technology 1 and 2, 529–532 (2004)

  31. Kurzhals, K., Fisher, B.D., Burch, M., Weiskopf, D.: Eye tracking evaluation of visual analytics. Information Visualization 15(4), 340–358 (2016). https://doi.org/10.1177/1473871615609787

    Article  Google Scholar 

  32. Pinggera, J.: The Process of Process Modeling. Ph.D. thesis, University of Innsbruck, Department of Computer Science (2014)

  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). https://doi.org/10.1007/s10270-013-0349-1

    Article  Google Scholar 

  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). https://doi.org/10.1007/s10270-013-0349-1

    Article  Google Scholar 

  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)

  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)

  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). https://doi.org/10.1007/1-84628-062-1_23

  38. Sharp, H.: Interaction Design. Wiley, Hoboken (2011)

    Google Scholar 

  39. Shmueli, G., Bruce, P., Yahav, I., Patel, N., Lichtendahl, K.: Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. Wiley (2017). https://books.google.de/books?id=ETwuDwAAQBAJ

  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). https://doi.org/10.1109/TSE.2005.97

    Article  Google Scholar 

  41. Spence, R.: Information Visualization. Prentice Hall, Upper Saddle River (2007)

    Google Scholar 

  42. Surma, J.: Business Intelligence: Making Decisions Through Data Analytics. Business Expert Press, New York (2011)

    Book  Google Scholar 

  43. Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1983)

    Google Scholar 

  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)

  45. Zelkowitz, M.V., Wallace, D.R.: Experimental models for validating technology. Computer 31(5), 23–31 (1998). https://doi.org/10.1109/2.675630

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. University of Duisburg-Essen, Essen, Germany

    Jens Gulden

  2. Technical University of Denmark, Kongens Lyngby, Denmark

    Andrea Burattin, Amine A. Andaloussi & Barbara Weber

  3. University of St. Gallen, St. Gallen, Switzerland

    Barbara Weber

Authors
  1. Jens Gulden
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Burattin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amine A. Andaloussi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Barbara Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jens Gulden.

Additional information

Communicated by Selmin Nurcan and Rainer Schmidt.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Detailed descriptive statistics for timing dimension

Appendix: Detailed descriptive statistics for timing dimension

See Tables 10, 11, 12, and 13.

Table 10 Descriptive statistics for event characteristics and relative timing
Full size table
Table 11 Descriptive statistics for phase characteristics and relative timing
Full size table
Table 12 Descriptive statistics for event characteristics and absolute timing
Full size table
Table 13 Descriptive statistics for phase characteristics and absolute timing
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gulden, J., Burattin, A., Andaloussi, A.A. et al. From analytical purposes to data visualizations: a decision process guided by a conceptual framework and eye tracking. Softw Syst Model 19, 531–554 (2020). https://doi.org/10.1007/s10270-019-00742-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10270-019-00742-z

Keywords

Search

Navigation