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Object-Centric Process Mining: An Introduction

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Formal Methods for an Informal World (ICTAC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13490))

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Abstract

Initially, the focus of process mining was on processes evolving around a single type of objects, e.g., orders, order lines, payments, deliveries, or customers. In this simplified setting, each event refers to precisely one object and the automatically discovered process models describe the lifecycles of the selected objects. Dozens of process-discovery and conformance-checking techniques have been developed using this simplifying assumption. However, real-life processes are more complex and involve objects of multiple types interacting through shared activities. Object-centric process mining techniques start from event logs consisting of events and objects without imposing the classical constraints, i.e., an event may involve multiple objects of possibly different types. This paper introduces object-centric event logs and shows that many of the existing process-discovery and conformance-checking techniques can be adapted to this more holistic setting. This provides many opportunities, as demonstrated by examples and the tool support we developed.

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Notes

  1. 1.

    See the website www.processmining.org for an up-to-date overview of existing tools.

  2. 2.

    Note that labeling function l is applied to a sequence of transitions, i.e., \(l(\langle \rangle ) = \langle \rangle \), \(l( \langle t \rangle ) = \langle l(t)\rangle \) if \(t \in dom (l)\), \(l( \langle t \rangle ) = \langle \rangle \) if \(t \not \in dom (l)\), and \(l(\sigma \cdot \langle t \rangle ) = l(\sigma ) \cdot l(\langle t \rangle )\) for any \(\sigma \in T^*\).

References

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

    Book  Google Scholar 

  2. van der Aalst, W.M.P.: A practitioner’s guide to process mining: limitations of the directly-follows graph. Procedia Comput. Sci. 164, 321–328 (2019). Part of specital issue: International Conference on Enterprise Information Systems, CENTERIS 2019

    Article  Google Scholar 

  3. Aalst, W.M.P.: Object-centric process mining: dealing with divergence and convergence in event data. In: Ölveczky, P.C., Salaün, G. (eds.) SEFM 2019. LNCS, vol. 11724, pp. 3–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30446-1_1

    Chapter  Google Scholar 

  4. van der Aalst, W.M.P.: Foundations of process discovery. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. Lecture Notes in Business Information Processing, vol. 448, pp. 37–75. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_2

    Chapter  Google Scholar 

  5. van der Aalst, W.M.P.: Process mining: a 360 degrees overview. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. Lecture Notes in Business Information Processing, vol. 448, pp. 3–34. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_1

    Chapter  Google Scholar 

  6. van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. WIREs Data Min. Knowl. Discov. 2(2), 182–192 (2012)

    Article  Google Scholar 

  7. van der Aalst, W.M.P., Barthelmess, P., Ellis, C.A., Wainer, J.: Proclets: a framework for lightweight interacting workflow processes. Int. J. Coop. Inf. Syst. 10(4), 443–482 (2001)

    Article  Google Scholar 

  8. van der Aalst, W.M.P., Berti, A.: Discovering object-centric Petri nets. Fund. Inform. 175(1–4), 1–40 (2020)

    MathSciNet  MATH  Google Scholar 

  9. van der Aalst, W.M.P., Stahl, C.: Modeling Business Processes: A Petri Net Oriented Approach. MIT Press, Cambridge (2011)

    Book  MATH  Google Scholar 

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

    Article  Google Scholar 

  11. Artale, A., Kovtunova, A., Montali, M., van der Aalst, W.M.P.: Modeling and reasoning over declarative data-aware processes with object-centric behavioral constraints. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 139–156. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_11

    Chapter  Google Scholar 

  12. 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 

  13. Augusto, A., Conforti, R., Marlon, M., La Rosa, M., Polyvyanyy, A.: Split miner: automated discovery of accurate and simple business process models from event logs. Knowl. Inf. Syst. 59(2), 251–284 (2019)

    Article  Google Scholar 

  14. Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process mining based on regions of languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_27

    Chapter  Google Scholar 

  15. Berti, A., van der Aalst, W.: Extracting multiple viewpoint models from relational databases. In: Ceravolo, P., van Keulen, M., Gómez-López, M.T. (eds.) SIMPDA 2018-2019. LNBIP, vol. 379, pp. 24–51. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46633-6_2

    Chapter  Google Scholar 

  16. Carmona, J., Cortadella, J., Kishinevsky, M.: A region-based algorithm for discovering Petri nets from event logs. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 358–373. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85758-7_26

    Chapter  Google Scholar 

  17. Carmona, J., van Dongen, B., Solti, A., Weidlich, M.: Conformance Checking. Relating Processes and Models. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99414-7

    Book  Google Scholar 

  18. Desel, J., Esparza, J.: Free choice Petri nets. In: Cambridge Tracts in Theoretical Computer Science, vol. 40. Cambridge University Press, Cambridge (1995)

    Google Scholar 

  19. Desel, J., Reisig, W.: Place/transition Petri nets. In: Reisig, W., Rozenberg, G. (eds.) ACPN 1996. LNCS, vol. 1491, pp. 122–173. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-65306-6_15

    Chapter  MATH  Google Scholar 

  20. Dumas, M., La Rosa, M., Mendling, J., Reijers, H.: Fundamentals of Business Process Management. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56509-4

    Book  Google Scholar 

  21. van Eck, M.L., Sidorova, N., van der Aalst, W.M.P.: Guided interaction exploration and performance analysis in artifact-centric process models. Bus. Inf. Syst. Eng. 61(6), 649–663 (2019)

    Article  Google Scholar 

  22. Fahland, D.: Describing behavior of processes with many-to-many interactions. In: Donatelli, S., Haar, S. (eds.) PETRI NETS 2019. LNCS, vol. 11522, pp. 3–24. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21571-2_1

    Chapter  Google Scholar 

  23. Fahland, D., de Leoni, M., van Dongen, B.F., van der Aalst, W.M.P.: Behavioral conformance of artifact-centric process models. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 37–49. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21863-7_4

    Chapter  Google Scholar 

  24. Fahland, D., de Leoni, M., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking of interacting processes with overlapping instances. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 345–361. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23059-2_26

    Chapter  Google Scholar 

  25. Galic, G., Wolf, M.: Global Process Mining Survey 2021: Delivering Value with Process Analytics - Adoption and Success Factors of Process Mining. Deloitte (2021)

    Google Scholar 

  26. Ghahfarokhi, A.F., Park, G., Berti, A., van der Aalst, W.M.P.: OCEL Standard (2021). www.ocel-standard.org

  27. 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 

  28. IEEE Task Force on Process Mining. XES Standard Definition (2016). www.xes-standard.org

  29. Jensen, K., Kristensen, L.M.: Coloured Petri Nets. Springer, Heidelberg (2009). https://doi.org/10.1007/b95112

    Book  MATH  Google Scholar 

  30. Kerremans, M., Srivastava, T., Choudhary, F.: Market Guide for Process Mining. Research Note G00737056. Gartner (2021). www.gartner.com

  31. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17

    Chapter  Google Scholar 

  32. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6

    Chapter  Google Scholar 

  33. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Scalable process discovery and conformance checking. Softw. Syst. Model. 17(2), 599–631 (2018)

    Article  Google Scholar 

  34. Li, G., de Carvalho, R.M., van der Aalst, W.M.P.: Automatic discovery of object-centric behavioral constraint models. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 43–58. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59336-4_4

    Chapter  Google Scholar 

  35. Lu, X., Nagelkerke, M., van de Wiel, D., Fahland, D.: Discovering interacting artifacts from ERP systems. IEEE Trans. Serv. Comput. 8(6), 861–873 (2015)

    Article  Google Scholar 

  36. OMG: Business Process Model and Notation (BPMN), Version 2.0.2. Object Management Group (2014). www.omg.org/spec/BPMN/

  37. 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 

  38. Solé, M., Carmona, J.: Process mining from a basis of state regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13675-7_14

    Chapter  MATH  Google Scholar 

  39. Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_11

    Chapter  Google Scholar 

  40. De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. In: IEEE Congress on Evolutionary Computation, CEC 2012, pp. 1–8. IEEE Computer Society (2012)

    Google Scholar 

  41. van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. Fund. Inform. 94, 387–412 (2010)

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

The author thanks the Alexander von Humboldt (AvH) Stiftung for supporting his research. Funded by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy, Internet of Production (390621612).

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  1. Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

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  1. Wil M. P. van der Aalst
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Correspondence to Wil M. P. van der Aalst .

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  1. Nazarbayev University, Astana, Kazakhstan

    Antonio Cerone

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van der Aalst, W.M.P. (2023). Object-Centric Process Mining: An Introduction. In: Cerone, A. (eds) Formal Methods for an Informal World. ICTAC 2021. Lecture Notes in Computer Science, vol 13490. Springer, Cham. https://doi.org/10.1007/978-3-031-43678-9_3

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