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

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Software Engineering and Formal Methods (SEFM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11724))

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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.

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Notes

  1. 1.

    Multisets are represented using square brackets, e.g., \(M=[x^2,y^3,z]\) has six elements. Unlike sets the same element can appear multiple times: \(M(x)=2\), \(M(y)=3\), and \(M(z)=1\). \([f(x) \mid x \in X]\) creates a multiset, i.e., if multiple elements x map onto the same value f(x), these are counted multiple times.

  2. 2.

    is the powerset of the universe of object identifiers, i.e., objects types are mapped onto sets of object identifiers.

  3. 3.

    \(\mathbb {U}_{ att } \not \rightarrow \mathbb {U}_{ val }\) is the set of all partial functions mapping a subset of attribute names onto the corresponding values.

  4. 4.

    \(R^*\) is the transitive closure of relation R. Hence, \(\preceq _E^{ ot }\) is a partial order (reflexive, antisymmetric, and transitive).

References

  1. van der Aalst, W.M.P.: The application of Petri Nets to workflow management. J. Circ. Syst. Comput. 8(1), 21–66 (1998)

    Article  Google Scholar 

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

    Book  Google Scholar 

  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. 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. 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. 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_20

    Chapter  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., et al.: Soundness of workflow nets: classification, decidability, and analysis. Form. Asp. Comput. 23(3), 333–363 (2011)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  10. van der Aalst, W.M.P., Li, G., Montali, M.: Object-Centric Behavioral Constraints. CoRR, abs/1703.05740 (2017)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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_21

    Chapter  Google Scholar 

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

    Book  Google Scholar 

  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. 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_15

    Chapter  Google Scholar 

  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. 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 

  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_4

    Chapter  Google Scholar 

  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. IBM. IBM MQSeries Workflow - Getting Started With Buildtime. IBM Deutschland Entwicklung GmbH, Boeblingen, Germany (1999)

    Google Scholar 

  23. IEEE Task Force on Process Mining. XES Standard Definition (2013). http://www.xes-standard.org/

  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. 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_16

    Chapter  Google Scholar 

  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_4

    Chapter  Google Scholar 

  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_11

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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_18

    Chapter  Google Scholar 

  30. Nigam, A., Caswell, N.S.: Business artifacts: an approach to operational specification. IBM Syst. J. 42(3), 428–445 (2003)

    Article  Google Scholar 

  31. OMG. Business Process Model and Notation (BPMN). Object Management Group, formal/2011-01-03 (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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_9

    Chapter  Google Scholar 

  34. Scheer, A.W.: Business Process Engineering: Reference Models for Industrial Enterprises. Springer, Heidelberg (1994). https://doi.org/10.1007/978-3-642-79142-0

    Book  Google Scholar 

  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_2

    Chapter  Google Scholar 

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Acknowledgments

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

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Authors and Affiliations

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

    Wil M. P. van der Aalst

  2. Fraunhofer Institute for Applied Information Technology, Sankt Augustin, 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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Editors and Affiliations

  1. University of Oslo, Oslo, Norway

    Peter Csaba Ölveczky

  2. University of Grenoble Alpes, Montbonnot, France

    Gwen Salaün

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van der Aalst, W.M.P. (2019). Object-Centric Process Mining: Dealing with Divergence and Convergence in Event Data. In: Ölveczky, P., Salaün, G. (eds) Software Engineering and Formal Methods. SEFM 2019. Lecture Notes in Computer Science(), vol 11724. Springer, Cham. https://doi.org/10.1007/978-3-030-30446-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-30446-1_1

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