Skip to main content
SpringerLink
Log in

Who Is Behind the Model? Classifying Modelers Based on Pragmatic Model Features

  • Conference paper
  • First Online:
Business Process Management (BPM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11080))

Included in the following conference series:

Abstract

Process modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See external appendix for details: https://doi.org/10.5281/zenodo.1251633.

  2. 2.

    The complete source code of the implementation is available at https://github.com/DTU-SPE/ExpertisePredictor4BPMN.

  3. 3.

    The dataset is available at https://doi.org/10.5281/zenodo.1194780.

  4. 4.

    See http://www.cs.waikato.ac.nz/ml/weka/.

  5. 5.

    Please note that the data collected from the practitioners has not been published before. Moreover, the model features used as basis for this paper have not been reported before, neither for students nor for practitioners.

  6. 6.

    Graphical representations on the appendix: https://doi.org/10.5281/zenodo.1251633.

References

  1. Burton-Jones, A., Meso, P.: The effects of decomposition quality and multiple forms of information on novices’ understanding of a domain from a conceptual model. J. AIS 9(12), 748–802 (2008)

    Google Scholar 

  2. Fettke, P.: How conceptual modeling is used. Commun. AIS (CAIS) 25, 571–592 (2009)

    Google Scholar 

  3. Recker, J., Safrudin, N., Rosemann, M.: How novices design business processes. Inf. Syst. 37(6), 557–573 (2012)

    Article  Google Scholar 

  4. Soffer, P., Kaner, M., Wand, Y.: Towards understanding the process of process modeling: theoretical and empirical considerations. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 357–369. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_35

    Chapter  Google Scholar 

  5. Wickens, C.D., Hollands, J.G.: Engineering Psychology and Human Performance, 3rd edn. Pearson, London (1999)

    Google Scholar 

  6. Figl, K.: Comprehension of procedural visual business process models a literature review. Bus. Inf. Syst. Eng. 59, 41–67 (2017)

    Article  Google Scholar 

  7. Koschmider, A., Reijers, H.A.: Improving the process of process modelling by the use of domain process patterns. Enterp. IS 9(1), 29–57 (2015)

    Google Scholar 

  8. Koschmider, A., Hornung, T., Oberweis, A.: Recommendation-based editor for business process modeling. Data Knowl. Eng. 70(6), 483–503 (2011)

    Article  Google Scholar 

  9. Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features - enhancing flexibility in process-aware information systems. Data Knowl. Eng. 66(3), 438–466 (2008)

    Article  Google Scholar 

  10. Gschwind, T., Koehler, J., Wong, J.: Applying patterns during business process modeling. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 4–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85758-7_4

    Chapter  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Claes, J., Vanderfeesten, I., Pinggera, J., Reijers, H.A., Weber, B., Poels, G.: Visualizing the Process of process modeling with PPMCharts. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 744–755. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_75

    Chapter  Google Scholar 

  13. Pinggera, J., et al.: Styles in business process modeling: an exploration and a model. Softw. Syst. Model. 14, 1055–1080 (2013)

    Article  Google Scholar 

  14. Krogstie, J.: Quality of models. In: Krogstie, J. (ed.) Model-Based Development and Evolution of Information Systems, pp. 205–247. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2936-3_4

    Chapter  Google Scholar 

  15. Mendling, J., Recker, J.C., Reijers, H., Leopold, H.: An empirical review of the connection between model viewer characteristics and the comprehension of conceptual process models. Inf. Syst. Front., 1–25 (2018)

    Google Scholar 

  16. Larkin, J., McDermott, J., Simon, D.P., Simon, H.A.: Expert and novice performance in solving physics problems. Science 208(4450), 1335–1342 (1980)

    Article  Google Scholar 

  17. Reijers, H.A., Mendling, J.: A study into the factors that influence the understandability of business process models. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 41(3), 449–462 (2011)

    Article  Google Scholar 

  18. Petre, M.: Why looking isn’t always seeing: readership skills and graphical programming. Commun. ACM 38(6), 33–44 (1995)

    Article  Google Scholar 

  19. Batra, D., Davis, J.G.: Conceptual data modelling in database design: similarities and differences between expert and novice designers. Int. J. Man Mach. Stud. 37(1), 83–101 (1992)

    Article  Google Scholar 

  20. Narasimha, B., Leung, F.S.: Assisting novice analysts in developing quality conceptual models with UML. Commun. ACM 49(7), 108–112 (2006)

    Article  Google Scholar 

  21. Jawaheer, G., Weller, P., Kostkova, P.: Modeling user preferences in recommender systems: a classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst. 4(2) (2014). Article no. 8

    Article  Google Scholar 

  22. Riedl, R., Léger, P.-M.: Fundamentals of NeuroIS-Information Systems and the Brain. SNPBE. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-45091-8

    Book  Google Scholar 

  23. Crk, I., Kluthe, T., Stefik, A.: Understanding programming expertise: an empirical study of phasic brain wave changes. ACM Trans. Comput.-Hum. Interact. 23(1), 2:1–2:29 (2016)

    Google Scholar 

  24. Pinggera, J.: The process of process modeling. Ph.D. thesis, University of Innsbruck (2014)

    Google Scholar 

  25. Martini, M., Pinggera, J., Neurauter, M., Sachse, P., Furtner, M.R., Weber, B.: The impact of working memory and the process of process modelling on model quality: investigating experienced versus inexperienced modellers. Sci. Rep. 6 (2016). Article no. 25561

    Google Scholar 

  26. Aggarwal, C.C.: Data Mining. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14142-8

    Book  MATH  Google Scholar 

  27. Baker, R.: Big Data and Education. Columbia University, New York (2015)

    Google Scholar 

  28. Mendling, J., Reijers, H.A., van der Aalst, W.M.P.: Seven process modeling guidelines (7PMG). Inf. Softw. Technol. 52(2), 127–136 (2010)

    Article  Google Scholar 

  29. Polyvyanyy, A.: Structuring process models. Ph.D. thesis, University of Potsdam (2012)

    Google Scholar 

  30. Haisjackl, C., Soffer, P., Lim, S.Y., Weber, B.: How do humans inspect BPMN models: an exploratory study. Softw. Syst. Model. 17, 655–673 (2016)

    Article  Google Scholar 

  31. Bernstein, V., Soffer, P.: Identifying and quantifying visual layout features of business process models. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) CAISE 2015. LNBIP, vol. 214, pp. 200–213. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19237-6_13

    Chapter  Google Scholar 

  32. Gschwind, T., Pinggera, J., Zugal, S., Reijers, H.A., Weber, B.: A linear time layout algorithm for business process models. JVLC 25(2), 117–132 (2014)

    Google Scholar 

  33. Figl, K., Strembeck, M.: On the importance of flow direction in business process models. In: Proceedings of ICSOFT-EA, pp. 132–136 (2014)

    Google Scholar 

  34. Burattin, A., Bernstein, V., Neurauter, M., Soffer, P., Weber, B.: Detection and quantification of flow consistency in business process models. SoSyM 17(2), 633–654 (2017)

    Google Scholar 

  35. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York City (1997)

    MATH  Google Scholar 

  36. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)

    Article  MathSciNet  Google Scholar 

  37. Huang, G.B.: Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14(2), 274–281 (2003)

    Article  Google Scholar 

  38. Hagan, M., Demuth, H., Beale, M., De Jesús, O.: Neural Network Design (2014). Oklahoma

    Google Scholar 

  39. Pinggera, J., Zugal, S., Weber, B.: Investigating the process of process modeling with cheetah experimental platform. In: Proceedings of the ER-POIS, pp. 13–15 (2010)

    Google Scholar 

  40. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, 1st edn. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  41. Niknafs, A., Berry, D.: The impact of domain knowledge on the effectiveness of requirements engineering activities. Empir. Softw. Eng. 22(1), 80–133 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the Austrian Science Fund (FWF): P26140–N15 and P26609N15.

Author information

Authors and Affiliations

  1. Technical University of Denmark, Kgs. Lyngby, Denmark

    Andrea Burattin & Barbara Weber

  2. University of Haifa, Haifa, Israel

    Pnina Soffer

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Dirk Fahland, Hajo A. Reijers & Irene Vanderfeesten

  4. Vienna University of Economics and Business, Vienna, Austria

    Jan Mendling

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Hajo A. Reijers

  6. Humboldt-University, Berlin, Germany

    Matthias Weidlich

  7. University of Innsbruck, Innsbruck, Austria

    Barbara Weber

Authors
  1. Andrea Burattin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pnina Soffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dirk Fahland
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jan Mendling
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hajo A. Reijers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Irene Vanderfeesten
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matthias Weidlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Barbara Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Burattin .

Editor information

Editors and Affiliations

  1. Hasso-Plattner-Institute, University of Potsdam, Potsdam, Germany

    Mathias Weske

  2. Free University of Bozen Bolzano, Bolzano, Italy

    Marco Montali

  3. Data61, CSIRO, Eveleigh, NSW, Australia

    Ingo Weber

  4. University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Burattin, A. et al. (2018). Who Is Behind the Model? Classifying Modelers Based on Pragmatic Model Features. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham. https://doi.org/10.1007/978-3-319-98648-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98648-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98647-0

  • Online ISBN: 978-3-319-98648-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation