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All that Glitters Is Not Gold

Towards Process Discovery Techniques with Guarantees

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Advanced Information Systems Engineering (CAiSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12751))

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Abstract

The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the input event data, the better the quality of the resulting discovered model should be. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

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Notes

  1. 1.

    The source code is available on: https://github.com/ArchitectureMining/SamplingFramework.

References

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

    Book  Google Scholar 

  2. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19

    Chapter  Google Scholar 

  3. van der Aalst, W.M.P.: Relating process models and event logs–21 conformance propositions. In: ATAED, volume 2115 of CEUR Workshop Proceedings, pp. 56–74. CEUR-WS.org (2018)

    Google Scholar 

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

    Article  Google Scholar 

  5. Augusto, A., Conforti, R., Dumas, M., La Rosa, M.: Split miner: discovering accurate and simple business process models from event logs. In: ICDM 2017, pp. 1–10. IEEE (2017)

    Google Scholar 

  6. Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE Trans. Knowl. Data Eng. 31(4), 686–705 (2019)

    Article  Google Scholar 

  7. Bauer, M., Senderovich, A., Gal, A., Grunske, L., Weidlich, M.: How much event data is enough? A statistical framework for process discovery. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 239–256. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_15

    Chapter  Google Scholar 

  8. Berti, A.: Statistical sampling in process mining discovery. In: eKNOW 2017, pp. 41–43. IARIA (2017)

    Google Scholar 

  9. Bose, J.C., Mans, R.S., van der Aalst, W.M.P.: Wanna improve process mining results? In: CIDM 2013, pp. 127–134. IEEE (2013)

    Google Scholar 

  10. Bozkaya, M., Gabriels, J.M.A.M., van der Werf, J.M.E.M.: Process diagnostics : a method based on process mining. In: eKNOW 2009, pp. 22–27. IEEE (2009)

    Google Scholar 

  11. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Quality dimensions in process discovery: the importance of fitness, precision, generalization and simplicity. Int. J. Coop. Inf. Syst. 23(1), 1440001 (2014)

    Article  Google Scholar 

  12. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press Ltd, Cambridge (2009)

    MATH  Google Scholar 

  13. de Leoni, M., Mannhardt, F.: Road Traffic Fine Management Process, February 2015. https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5

  14. 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 

  15. van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM\(^2\): a process mining project methodology. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_19

    Chapter  Google Scholar 

  16. Günther, C.: Process mining in flexible environments. Ph.D. thesis, Eindhoven University of Technology (2009)

    Google Scholar 

  17. Knols, B., van der Werf, J.M.E.M.: Measuring the behavioral quality of log sampling. In: ICPM 2019, pp. 97–104. IEEE (2019

    Google Scholar 

  18. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Scalable process discovery with guarantees. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) CAISE 2015. LNBIP, vol. 214, pp. 85–101. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19237-6_6

    Chapter  Google Scholar 

  19. Liu, C., Pei, Y., Zeng, Q., Duan, H.: LogRank: an approach to sample business process event log for efficient discovery. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11061, pp. 415–425. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99365-2_36

    Chapter  Google Scholar 

  20. Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: Efficient discovery of understandable declarative process models from event logs. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 270–285. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31095-9_18

    Chapter  Google Scholar 

  21. Mannhardt, F.: Sepsis Cases - Event Log, December 2016. https://doi.org/10.4121/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460

  22. Polyvyanyy, A., et al.: Entropia: a family of entropy-based conformance checking measures for process mining. In: ICPM Doctoral Consortium and Tool Demonstration, volume 2703 of CEUR, pp. 39–42. CEUR-WS.org (2020)

    Google Scholar 

  23. Polyvyanyy, A., Kalenkova, A.A.: Monotone conformance checking for partially matching designed and observed processes. In: ICPM 2019, pp. 81–88 (2019)

    Google Scholar 

  24. Polyvyanyy, A., Solti, A., Weidlich, M., Di Ciccio, C., Mendling, J.: Monotone precision and recall measures for comparing executions and specifications of dynamic systems. ACM Trans. Softw. Eng. Methodol. 29(3), 17:1–17:41 (2020)

    Article  Google Scholar 

  25. Rehse, J.-R., Fettke, P.: Process mining crimes – a threat to the validity of process discovery evaluations. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNBIP, vol. 329, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98651-7_1

    Chapter  Google Scholar 

  26. Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Improving the performance of process discovery algorithms by instance selection. Comput. Sci. Inf. Syst. 17(3), 927–958 (2020)

    Google Scholar 

  27. Syring, A.F., Tax, N., van der Aalst, W.M.P.: Evaluating conformance measures in process mining using conformance propositions. In: Koutny, M., Pomello, L., Kristensen, L.M. (eds.) Transactions on Petri Nets and Other Models of Concurrency XIV. LNCS, vol. 11790, pp. 192–221. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-662-60651-3_8

    Chapter  Google Scholar 

  28. Tax, N., Lu, X., Sidorova, N., Fahland, D., van der Aalst, W.M.P.: The imprecisions of precision measures in process mining. Inf. Process. Lett. 135, 1–8 (2018)

    Article  MathSciNet  Google Scholar 

  29. Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: CIDM 2011, pp. 310–317. IEEE (2011)

    Google Scholar 

  30. van Wensveen, B.R.: Estimation and analysis of the quality of event log samples for process discovery. Master’s thesis, Utrecht University (2020). https://dspace.library.uu.nl/handle/1874/400143

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

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

Artem Polyvyanyy was in part supported by the Australian Research Council project DP180102839.

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Authors and Affiliations

  1. Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Jan Martijn E. M. van der Werf, Bart R. van Wensveen, Matthieu Brinkhuis & Hajo A. Reijers

  2. The University of Melbourne, Parkville, VIC, 3010, Australia

    Artem Polyvyanyy

Authors
  1. Jan Martijn E. M. van der Werf
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  2. Artem Polyvyanyy
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  3. Bart R. van Wensveen
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  4. Matthieu Brinkhuis
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  5. Hajo A. Reijers
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Corresponding author

Correspondence to Jan Martijn E. M. van der Werf .

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Editors and Affiliations

  1. The University of Melbourne, Melbourne, VIC, Australia

    Marcello La Rosa

  2. The University of Queensland, St Lucia, QLD, Australia

    Shazia Sadiq

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Ernest Teniente

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van der Werf, J.M.E.M., Polyvyanyy, A., van Wensveen, B.R., Brinkhuis, M., Reijers, H.A. (2021). All that Glitters Is Not Gold. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-79382-1_9

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  • Online ISBN: 978-3-030-79382-1

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