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Object-Centric Process Mining: Dealing with Divergence and Convergence in Event Data

  • Wil M. P. van der AalstEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11724)

Abstract

Process mining techniques use event data to answer a variety of process-related questions. Process discovery, conformance checking, model enhancement, and operational support are used to improve performance and compliance. Process mining starts from recorded events that are characterized by a case identifier, an activity name, a timestamp, and optional attributes like resource or costs. In many applications, there are multiple candidate identifiers leading to different views on the same process. Moreover, one event may be related to different cases (convergence) and, for a given case, there may be multiple instances of the same activity within a case (divergence). To create a traditional process model, the event data need to be “flattened”. There are typically multiple choices possible, leading to different views that are disconnected. Therefore, one quickly loses the overview and event data need to be exacted multiple times (for the different views). Different approaches have been proposed to tackle the problem. This paper discusses the gap between real event data and the event logs required by traditional process mining techniques. The main purpose is to create awareness and to provide ways to characterize event data. A specific logging format is proposed where events can be related to objects of different types. Moreover, basic notations and a baseline discovery approach are presented to facilitate discussion and understanding.

Keywords

Process mining Process discovery Divergence Convergence Artifact-centric modeling 

Notes

Acknowledgments

We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.

References

  1. 1.
    van der Aalst, W.M.P.: The application of Petri Nets to workflow management. J. Circ. Syst. Comput. 8(1), 21–66 (1998)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P.: Discovering the “Glue” connecting activities - exploiting monotonicity to learn places faster. In: de Boer, F., Bonsangue, M., Rutten, J. (eds.) It’s All About Coordination. Lecture Notes in Computer Science, pp. 1–20. Springer-Verlag, Berlin (2018)Google Scholar
  4. 4.
    Berti, A., van der Aalst, W.M.P.: StarStar models: using events at database level for process analysis. In: Ceravolo, P., Gomez Lopez, M.T., van Keulen, M. (eds.) International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2018), volume 2270 of CEUR Workshop Proceedings, pp. 60–64. CEUR-WS.org (2018)Google Scholar
  5. 5.
    van der Aalst, Artale, A., Montali, M., Tritini, S.: Object-centric behavioral constraints: integrating data and declarative process modelling. In: Proceedings of the 30th International Workshop on Description Logics (DL 2017), volume 1879 of CEUR Workshop Proceedings. CEUR-WS.org (2017)Google Scholar
  6. 6.
    van der Aalst, W.M.P., Barthelmess, P., Ellis, C.A., Wainer, J.: Workflow modeling using proclets. In: Scheuermann, P., Etzion, O. (eds.) CoopIS 2000. LNCS, vol. 1901, pp. 198–209. Springer, Heidelberg (2000).  https://doi.org/10.1007/10722620_20CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    van der Aalst, W.M.P., et al.: Soundness of workflow nets: classification, decidability, and analysis. Form. Asp. Comput. 23(3), 333–363 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distrib. Parallel Databases 14(1), 5–51 (2003)CrossRefGoogle Scholar
  10. 10.
    van der Aalst, W.M.P., Li, G., Montali, M.: Object-Centric Behavioral Constraints. CoRR, abs/1703.05740 (2017)Google Scholar
  11. 11.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. Comput. Sci.-Res. Dev. 23(2), 99–113 (2009)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Artale, A., Calvanese, D., Montali, M., van der Aalst, W.M.P.: Enriching data models with behavioral constraints. In: Borgo, S. (ed.) Ontology Makes Sense (Essays in honor of Nicola Guarino), pp. 257–277. IOS Press (2019)Google Scholar
  14. 14.
    Bhattacharya, K., Gerede, C., Hull, R., Liu, R., Su, J.: Towards formal analysis of artifact-centric business process models. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 288–304. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75183-0_21CrossRefGoogle Scholar
  15. 15.
    Carmona, J., van Dongen, B., Solti, A., Weidlich, M.: Conformance Checking: Relating Processes and Models. Springer, Heidelberg (2018).  https://doi.org/10.1007/978-3-319-99414-7CrossRefGoogle Scholar
  16. 16.
    Cohn, D., Hull, R.: Business artifacts: a data-centric approach to modeling business operations and processes. IEEE Data Eng. Bull. 32(3), 3–9 (2009)Google Scholar
  17. 17.
    González López de Murillas, E., Reijers, H.A., van der Aalst, W.M.P.: Connecting databases with process mining: a meta model and toolset. In: Schmidt, R., Guédria, W., Bider, I., Guerreiro, S. (eds.) BPMDS/EMMSAD -2016. LNBIP, vol. 248, pp. 231–249. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39429-9_15CrossRefGoogle Scholar
  18. 18.
    van Eck, M.L., Sidorova, N., van der Aalst, W.M.P.: Guided interaction exploration in artifact-centric process models. In: IEEE Conference on Business Informatics (CBI 2017), pp. 109–118. IEEE Computer Society (2017)Google Scholar
  19. 19.
    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_1CrossRefGoogle Scholar
  20. 20.
    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_4CrossRefGoogle Scholar
  21. 21.
    Fahland, D., De Leoni, M., van Dongen, B., van der Aalst, W.M.P.: Many-to-many: some observations on interactions in artifact choreographies. In: Eichhorn, D., Koschmider, A., Zhang, H. (eds.) Proceedings of the 3rd Central-European Workshop on Services and Their Composition (ZEUS 2011), CEUR Workshop Proceedings, pp. 9–15. CEUR-WS.org (2011)Google Scholar
  22. 22.
    IBM. IBM MQSeries Workflow - Getting Started With Buildtime. IBM Deutschland Entwicklung GmbH, Boeblingen, Germany (1999)Google Scholar
  23. 23.
    IEEE Task Force on Process Mining. XES Standard Definition (2013). http://www.xes-standard.org/
  24. 24.
    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.) Applications and Theory of Petri Nets 2013. Lecture Notes in Computer Science, vol. 7927, pp. 311–329. Springer-Verlag, Berlin (2013)Google Scholar
  25. 25.
    Li, G., de Murillas, E.G.L., de Carvalho, R.M., van der Aalst, W.M.P.: Extracting object-centric event logs to support process mining on databases. In: Mendling, J., Mouratidis, H. (eds.) CAiSE 2018. LNBIP, vol. 317, pp. 182–199. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92901-9_16CrossRefGoogle Scholar
  26. 26.
    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_4CrossRefGoogle Scholar
  27. 27.
    Lohmann, N.: Compliance by design for artifact-centric business processes. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 99–115. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23059-2_11CrossRefGoogle Scholar
  28. 28.
    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)CrossRefGoogle Scholar
  29. 29.
    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_18CrossRefGoogle Scholar
  30. 30.
    Nigam, A., Caswell, N.S.: Business artifacts: an approach to operational specification. IBM Syst. J. 42(3), 428–445 (2003)CrossRefGoogle Scholar
  31. 31.
    OMG. Business Process Model and Notation (BPMN). Object Management Group, formal/2011-01-03 (2011)Google Scholar
  32. 32.
    Rovani, M., Maggi, F.M., de Leoni, M., van der Aalst, W.M.P.: Declarative process mining in healthcare. Expert Syst. Appl. 42(23), 9236–9251 (2015)CrossRefGoogle Scholar
  33. 33.
    Bose, R.P.J.C., Maggi, F.M., van der Aalst, W.M.P.: Enhancing declare maps based on event correlations. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 97–112. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40176-3_9CrossRefGoogle Scholar
  34. 34.
    Scheer, A.W.: Business Process Engineering: Reference Models for Industrial Enterprises. Springer, Heidelberg (1994).  https://doi.org/10.1007/978-3-642-79142-0CrossRefGoogle Scholar
  35. 35.
    van Eck, M.L., Sidorova, N., van der Aalst, W.M.P.: Multi-instance mining: discovering synchronisation in artifact-centric processes. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 18–30. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-11641-5_2CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Process and Data Science (PADS)RWTH Aachen UniversityAachenGermany
  2. 2.Fraunhofer Institute for Applied Information TechnologySankt AugustinGermany

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