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Transforming Event Knowledge Graph to Object-Centric Event Logs: A Comparative Study for Multi-dimensional Process Analysis

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Conceptual Modeling (ER 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14320))

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Abstract

Process mining has significantly transformed business process management by introducing innovative data-based analysis techniques and empowering organizations to unveil hidden insights previously buried within their recorded data. The analysis is conducted on event logs structured by conceptual models. Traditional models were defined based on only a single case notion, e.g., order or item in the purchase process. This limitation hinders the application of process mining in practice for which new data models are developed, a.k.a, multi-dimensional Event Knowledge Graph (EKG) and Object-Centric Event Log (OCEL). While several tools have been developed for OCEL, there is a lack of process mining tooling around the EKG. In addition, there is a lack of comparison about the practical implication of choosing one approach over the other. To fill this gap, the contribution of this paper is threefold. First, it defines and implements an algorithm to transform event logs represented as EKG to OCEL. The implementation is then used to transform five real event logs based on which the approach is evaluated. Second, it compares the performance of analyzing event logs represented in these two models. Third, it reveals similarities and differences in analyzing processes based on event logs represented in these two models. The results highlight ten important findings, including different approaches in calculating directly-follows relations when analyzing filtered event logs in these models and issues that need to be considered in analyzing event lifecycle and inter-log relations using OCEL.

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Notes

  1. 1.

    https://www.celonis.com/.

  2. 2.

    \(\mathcal{P}(\mathbb {U}_{ oid })\) 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.

    The definition is aligned with definitions in [14, 16].

  5. 5.

    The library can be installed using !pip install neo4pm.

  6. 6.

    The source code is available at https://github.com/neo4pm/neo4pm.

References

  1. The neo4j operations manual v5: Performance: Disks, ram and other tips. https://neo4j.com/docs/operations-manual/current/performance/disks-ram-and-other-tips. Accessed 05 Aug 2023

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

  3. Adams, J.N., Park, G., Levich, S., Schuster, D., van der Aalst, W.M.P.: A framework for extracting and encoding features from object-centric event data. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernandez, P., Ruiz-Cortes, A. (eds.) ICSOC 2022. LNCS, vol. 13740, pp. 36–53. Springer, Cham (2022)

    Chapter  Google Scholar 

  4. Adams, J.N., Park, G., van der Aalst, W.M.P.: ocpa: a python library for object-centric process analysis. Softw. Impacts 14, 100438 (2022)

    Article  Google Scholar 

  5. Adams, J.N., Schuster, D., Schmitz, S., Schuh, G., van der Aalst, W.M.P.: Defining cases and variants for object-centric event data. In: 2022 4th International Conference on Process Mining (ICPM), pp. 128–135. IEEE (2022)

    Google Scholar 

  6. Adams, J.N., van der Aalst, W.M.P.: Precision and fitness in object-centric process mining. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 128–135. IEEE (2021)

    Google Scholar 

  7. Adams, J.N., van der Aalst, W.M.P.: Oc \(\pi \): object-centric process insights. In: Bernardinello, L., Petrucci, L. (eds.) PETRI NETS 2022. LNCS, vol. 13288, pp. 139–150. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06653-5_8

    Chapter  Google Scholar 

  8. Adams, J.N., van Zelst, S.J., Rose, T., van der Aalst, W.M.P.: Explainable concept drift in process mining. Inf. Syst. 114, 102177 (2023)

    Article  Google Scholar 

  9. Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., Vrgoč, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. (CSUR) 50(5), 1–40 (2017)

    Article  Google Scholar 

  10. Berti, A., Park, G., Rafiei, M., van der Aalst, W.M.P.: An event data extraction approach from SAP ERP for process mining. In: ICPM Workshops, vol. 433, pp. 255–267 (2021)

    Google Scholar 

  11. Berti, A., van der Aalst, W.M.P.: OC-PM: analyzing object-centric event logs and process models. Int. J. Softw. Tools Technol. Transfer 25(1), 1–17 (2023)

    Article  Google Scholar 

  12. Berti, A., van Zelst, S., Schuster, D.: PM4Py: a process mining library for Python. Softw. Impacts 17, 100556 (2023)

    Article  Google Scholar 

  13. Berti, A., Van Zelst, S.J., van der Aalst, W.M.P.: Process mining for python (pm4py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169 (2019)

  14. Esser, S., Fahland, D.: Multi-dimensional event data in graph databases. J. Data Semant. 10(1–2), 109–141 (2021)

    Article  Google Scholar 

  15. Fahland, D.: Event Graph of BPI Challenge 2019 (2021). https://data.4tu.nl/articles/dataset/Event_Graph_of_BPI_Challenge_2019/14169614/1

  16. Fahland, D.: Process mining over multiple behavioral dimensions with event knowledge graphs. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNCS, vol. 448, pp. 274–319. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_9

    Chapter  Google Scholar 

  17. Fahland , D., Esser, S.: Event graph of BPI challenge 2014 (2021). https://data.4tu.nl/articles/dataset/Event_Graph_of_BPI_Challenge_2014/14169494/1

  18. Fahland, D., Esser, S.: Event Graph of BPI Challenge 2015 (2021). https://data.4tu.nl/articles/dataset/Event_Graph_of_BPI_Challenge_2015/14169569/1

  19. Fahland, D., Esser, S.: Event Graph of BPI Challenge 2016 (2021). https://data.4tu.nl/articles/dataset/Event_Graph_of_BPI_Challenge_2016/14164220

  20. Fahland, D., Esser, S.: Event Graph of BPI Challenge 2017 (2021). https://data.4tu.nl/articles/dataset/Event_Graph_of_BPI_Challenge_2017/14169584/1

  21. Ghahfarokhi, A.F., Park, G., Berti, A., van der Aalst, W.M.P.: OCEL: a standard for object-centric event logs. In: Bellatreche, L., et al. (eds.) ADBIS 2021. CCIS, vol. 1450, pp. 169–175. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85082-1_16

    Chapter  Google Scholar 

  22. Gherissi, W., El Haddad, J., Grigori, D.: Object-centric predictive process monitoring. In: Troya, J., et al. (eds.) ICSOC 2022. LNCS, vol. 13821, pp. 27–39. Springer, Cham (2023)

    Google Scholar 

  23. Gunther, C.W., Verbeek, H.: Xes-standard definition (2014)

    Google Scholar 

  24. Hogan, A., et al.: Knowledge graphs. ACM Comput. Surv. (CSUR) 54(4), 1–37 (2021)

    Article  Google Scholar 

  25. Jalali, A.: Graph-based process mining. In: Leemans, S., Leopold, H. (eds.) ICPM 2020. LNBIP, vol. 406, pp. 273–285. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72693-5_21

    Chapter  Google Scholar 

  26. Jalali, A.: Object type clustering using Markov directly-follow multigraph in object-centric process mining. IEEE Access 10, 126569–126579 (2022)

    Article  Google Scholar 

  27. Jalali, A.: dfgcompare: a library to support process variant analysis through Markov models. BMC Med. Inf. Decis. Making 21(1), 1–13 (2021)

    Google Scholar 

  28. Khayatbashi, S., Hartig, O., Jalali, A.: BPI Challenge 2014 (OCEL) (2023). https://doi.org/10.4121/7d097cec-7304-4b85-9e78-a3ca1cc44c40

  29. Khayatbashi, S., Hartig, O., Jalali, A.: BPI Challenge 2015 (OCEL) (2023). https://doi.org/10.4121/110d2fcf-b5e1-494a-a588-896a0a21e60a

  30. Khayatbashi, S., Hartig, O., Jalali, A.: BPI Challenge 2016 (OCEL) (2023). https://doi.org/10.4121/95613fb2-29a5-49dc-b196-0948cf96cd7c

  31. Khayatbashi, S., Hartig, O., Jalali, A.: BPI Challenge 2017 (OCEL) (2023). https://doi.org/10.4121/6889ca3f-97cf-459a-b630-3b0b0d8664b5

  32. Khayatbashi, S., Hartig, O., Jalali, A.: BPI Challenge 2019 (OCEL) (2023). https://doi.org/10.4121/46a7e15b-10c7-4ab2-988d-ee67d8ea515a

  33. 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 

  34. 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 

  35. Park, G., Adams, J.N., van der Aalst, W.M.P.: Opera: object-centric performance analysis. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds.) ER 2022. LNCS, vol. 13607, pp. 281–292. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17995-2_20

    Chapter  Google Scholar 

  36. Park, G., van der Aalst, W.M.P.: Monitoring constraints in business processes using object-centric constraint graphs. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 479–492. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27815-0_35

    Chapter  Google Scholar 

  37. Rebmann, A., Rehse, J.R., van der Aa, H.: Uncovering object-centric data in classical event logs for the automated transformation from XES to OCEL. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds.) BPM 2022. LNCS, vol. 13420, pp. 379–396. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16103-2_25

    Chapter  Google Scholar 

  38. van der Aalst, W.M.P.: Process Mining: Data Science in Action, vol. 2. Springer, Heidelberg (2016)

    Book  Google Scholar 

  39. van der 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

  40. van der Aalst, W.M.P., Berti, A.: Discovering object-centric petri nets. Fundamenta informaticae 175(1–4), 1–40 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  41. van der Aalst, W.M.P., Gunther, C.W.: Finding structure in unstructured processes: The case for process mining. In: Seventh International Conference on Application of Concurrency to System Design (ACSD 2007), pp. 3–12. IEEE (2007)

    Google Scholar 

  42. Xiong, J., Xiao, G., Kalayci, T.E., Montali, M., Gu, Z., Calvanese, D.: A virtual knowledge graph based approach for object-centric event logs extraction. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 466–478. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27815-0_34

    Chapter  Google Scholar 

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Acknowledgements

Khayatbashi’s and Hartig’s contributions to this work were funded by Vetenskapsrådet (the Swedish Research Council, project reg. no. 2019-05655).

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Authors and Affiliations

  1. Linköping University, Linköping, Sweden

    Shahrzad Khayatbashi & Olaf Hartig

  2. Stockholm University, Stockholm, Sweden

    Amin Jalali

Authors
  1. Shahrzad Khayatbashi
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  2. Olaf Hartig
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  3. Amin Jalali
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Corresponding author

Correspondence to Shahrzad Khayatbashi .

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Editors and Affiliations

  1. Federal University of Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  2. Universidade de Lisboa, Lisbon, Portugal

    José Borbinha

  3. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

  4. University of Auckland, Auckland, New Zealand

    Sebastian Link

  5. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

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Khayatbashi, S., Hartig, O., Jalali, A. (2023). Transforming Event Knowledge Graph to Object-Centric Event Logs: A Comparative Study for Multi-dimensional Process Analysis. In: Almeida, J.P.A., Borbinha, J., Guizzardi, G., Link, S., Zdravkovic, J. (eds) Conceptual Modeling. ER 2023. Lecture Notes in Computer Science, vol 14320. Springer, Cham. https://doi.org/10.1007/978-3-031-47262-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-47262-6_12

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