Skip to main content
SpringerLink
Log in

Uncovering the Relationship Between Event Log Characteristics and Process Discovery Techniques

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

Included in the following conference series:

Abstract

The research field of process mining deals with the extraction of knowledge from event logs. Event logs consist of the recording of activities that took place in a certain business environment and as such, one of process mining’s main goals is to get an insight on the execution of business processes. Although a substantial effort has been put on developing techniques which are able to mine event logs accurately, it is still unclear how exactly characteristics of the latter influence a technique’s performance. In this paper, we provide a robust methodology of analysis and subsequently derive useful insights on the role of event log characteristics in process discovery tasks by means of an exhaustive comparative study.

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: http://www.cpntools.org

References

  1. van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  2. Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)

    Google Scholar 

  3. Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (fhm). In: [37], pp. 310–317

    Google Scholar 

  4. de Medeiros, A.K.A., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation. Data Min. Knowl. Discov. 14(2), 245–304 (2007)

    Article  MathSciNet  Google Scholar 

  5. van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)

    Google Scholar 

  6. Rozinat, A., de Medeiros, A.K.A, Günther, C.W., Weijters, A.J.M.M.T., van der Aalst, W.M.P.: The need for a process mining evaluation framework in research and practice. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y., et al. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 84–89. Springer, Heidelberg (2008)

    Google Scholar 

  7. Wang, J., Wong, R.K., Ding, J., Guo, Q., Wen, L.: On recommendation of process mining algorithms. In Goble, C.A., Chen, P.P., Zhang, J. (eds.) ICWS, pp. 311–318. IEEE (2012)

    Google Scholar 

  8. Weerdt, J.D., Backer, M.D., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012)

    Article  Google Scholar 

  9. Rozinat, A., Alves De Medeiros, A.K., Günther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: Towards an evaluation framework for process mining algorithms. BETA working paper series 224, Eindhoven University of Technology (2007)

    Google Scholar 

  10. Weber, P., Bordbar, B., Tino, P., Majeed, B.: A framework for comparing process mining algorithms. In: 2011 IEEE GCC Conference and Exhibition (GCC), pp. 625–628 (2011)

    Google Scholar 

  11. Wang, J., Wong, R., Ding, J., Guo, Q., Wen, L.: Efficient selection of process mining algorithms. IEEE Trans. Serv. Comput. PP(99), 1 (2012)

    Article  Google Scholar 

  12. Alves de Medeiros, A.K., van Dongen, B.F., van der Aalst, W.M.P., Weijters, A.J.M.M.: Process mining: extending the alpha-algorithm to mine short loops. BETA working paper series 113, TU Eindhoven (2004)

    Google Scholar 

  13. Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Disc. 15(2), 145–180 (2007)

    Article  Google Scholar 

  14. Alves de Medeiros, A.K.: Genetic Process Mining. Ph.D. thesis, TU Eindhoven (2006)

    Google Scholar 

  15. Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)

    Article  Google Scholar 

  16. Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. J. Mach. Learn. Res. 10, 1305–1340 (2009)

    MATH  MathSciNet  Google Scholar 

  17. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, June 2012

    Google Scholar 

  18. van der Aalst, W., Adriansyah, A., van Dongen, B.: Causal nets: a modeling language tailored towards process discovery. In: Katoen, J.-P., König, B. (eds.) CONCUR 2011. LNCS, vol. 6901, pp. 28–42. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. Fundam. Inform. 94(3–4), 387–412 (2009)

    MATH  Google Scholar 

  20. van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87–111 (2010)

    Article  Google Scholar 

  21. van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: Prom: the process mining toolkit. In: de Medeiros, A.K.A., Weber, B. (eds.) BPM (Demos). CEUR Workshop Proceedings, vol. 489. CEUR-WS.org (2009)

    Google Scholar 

  22. vanden Broucke, S., Weerdt, J.D., Baesens, B., Vanthienen, J.: A comprehensive benchmarking framework (cobefra) for conformance analysis between procedural process models and event logs in prom. In: IEEE Symposium on Computational Intelligence and Data Mining, Grand Copthorne Hotel, Singapore. IEEE (2013)

    Google Scholar 

  23. Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)

    Article  Google Scholar 

  24. Adriansyah, A., Sidorova, N., van Dongen, B.F.: Cost-based fitness in conformance checking. In Caillaud, B., Carmona, J., Hiraishi, K. (eds.) ACSD, pp. 57–66. IEEE (2011)

    Google Scholar 

  25. Weidlich, M., Polyvyanyy, A., Desai, N., Mendling, J.: Process compliance measurement based on behavioural profiles. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 499–514. Springer, Heidelberg (2010)

    Google Scholar 

  26. vanden Broucke, S.K.L.M., De Weerdt, J., Baesens, B., Vanthienen, J.: Improved artificial negative event generation to enhance process event logs. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 254–269. Springer, Heidelberg (2012)

    Google Scholar 

  27. Muñoz-Gama, J., Carmona, J.: A fresh look at precision in process conformance. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 211–226. Springer, Heidelberg (2010)

    Google Scholar 

  28. van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisc. Rew: Data Min. Knowl. Disc. 2(2), 182–192 (2012)

    Google Scholar 

  29. Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Alignment based precision checking. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013)

    Google Scholar 

  30. Weerdt, J.D., Backer, M.D., Vanthienen, J., Baesens, B.: A robust f-measure for evaluating discovered process models. In: [37], pp. 148–155

    Google Scholar 

  31. Sánchez-González, L., García, F., Mendling, J., Ruiz, F., Piattini, M.: Prediction of business process model quality based on structural metrics. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds.) ER 2010. LNCS, vol. 6412, pp. 458–463. Springer, Heidelberg (2010)

    Google Scholar 

  32. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MATH  MathSciNet  Google Scholar 

  33. Mauchly, J.W.: Significance test for sphericity of a normal n-variate distribution. Ann. Math. Stat. 11(2), 204–209 (1940)

    Article  MathSciNet  Google Scholar 

  34. Greenhouse, S.W., Geisser, S.: On methods in the analysis of profile data. Psychometrika 24(2), 95–112 (1959)

    Article  MathSciNet  Google Scholar 

  35. Huynh, H., Feldt, L.S.: Estimation of the box correction for degrees of freedom from sample data in randomized block and split-plot designs. J. Educ. Stat. 1(1), 69–82 (1976)

    Google Scholar 

  36. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., Panetto, H., Dillon, T., Rinderle-Ma, S., Dadam, P., Zhou, X., Pearson, S., Ferscha, A., Bergamaschi, S., Cruz, I.F. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012)

    Google Scholar 

  37. Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, April 11–15, 2011, Paris, France. IEEE (2011)

    Google Scholar 

Download references

Acknowledgment

We would like to thank the KU Leuven research council for financial support under grand OT/10/010 and the Flemish Research Council for financial support under Odysseus grant B.0915.09.

Author information

Authors and Affiliations

  1. Department of Decision Sciences and Information Management, KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Seppe K. L. M. vanden Broucke, Cédric Delvaux, João Freitas, Taisiia Rogova, Jan Vanthienen & Bart Baesens

  2. School of Management, University of Southampton, Highfield, Southampton, SO17 1BJ, UK

    Bart Baesens

Authors
  1. Seppe K. L. M. vanden Broucke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cédric Delvaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Freitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taisiia Rogova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jan Vanthienen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bart Baesens
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seppe K. L. M. vanden Broucke .

Editor information

Editors and Affiliations

  1. University of Rostock, Rostock, Germany

    Niels Lohmann

  2. Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South Korea)

    Minseok Song

  3. Stockholm University, Stockholm, Sweden

    Petia Wohed

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

vanden Broucke, S.K.L.M., Delvaux, C., Freitas, J., Rogova, T., Vanthienen, J., Baesens, B. (2014). Uncovering the Relationship Between Event Log Characteristics and Process Discovery Techniques. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06257-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation