Abstract
Changes in processes manifest as concept drift in event logs. Drift detection aids in analyzing the nature of such change and its impact on the process. Process executions or cases are driven by actors and machines performing the actual work. Actors typically divide and structure their work into tasks—multiple consecutive actions performed together—before handing a case to the next actor. Process changes affect this work division and collaboration, potentially impacting performance and outcomes. However, existing research on concept drift detection from event logs has not yet focused on the behavior of actors. We generalize an existing concept drift detection technique to consider actor behavior and control-flow jointly by using a multi-layered event knowledge graph. We evaluate our proposal by comparing the theoretical properties of the newly defined actor perspective features with existing features and perform an experimental evaluation. The experiments showed actor features to be more robust with on average (up to factor 2.6) stronger signals for concept drift in two real-life datasets. Our approach led to new insights into global process changes, changes in behavior of individual actors, and change in collaborations between actors.
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Full BPIC’17 results available: https://zenodo.org/doi/10.5281/zenodo.10933096.
References
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)
Aggarwal, C.C.: Data Mining - The Textbook. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14142-8
Bonifati, A., Fletcher, G.H.L., Voigt, H., Yakovets, N.: Querying Graphs. Synthesis Lectures on Data Management. Morgan & Claypool Publishers (2018)
Bose, R.P.J.C., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2014)
Delcoucq, L., Lecron, F., Fortemps, P., van der Aalst, W.M.P.: Resource-centric process mining: clustering using local process models. In: SAC 2020, pp. 45–52. ACM (2020)
van Dongen, B.F.: BPI challenge 2017. Dataset (2017). https://doi.org/10.4121/12705737.v2
Dumas, M., et al.: AI-augmented business process management systems: a research manifesto. ACM Trans. Manag. Inf. Syst. 14(1), 11:1–11:19 (2023)
El-Khawaga, G., Abu-Elkheir, M., Barakat, S.I., Riad, A.M., Reichert, M.: CONDA-PM - a systematic review and framework for concept drift analysis in process mining. Algorithms 13(7), 161 (2020)
Esser, S., Fahland, D.: Multi-dimensional event data in graph databases. J. Data Semant. 10, 109–141 (2021)
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. LNBIP, vol. 448, pp. 274–319. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_9
van Hulzen, G.A.W.M., Li, C., Martin, N., van Zelst, S.J., Depaire, B.: Mining context-aware resource profiles in the presence of multitasking. Artif. Intell. Med. 134, 102434 (2022)
Jans, M., Eulerich, M.: Process mining for financial auditing. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 445–467. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_15
Jooken, L., Jans, M., Depaire, B.: Mining valuable collaborations from event data using the recency-frequency-monetary principle. In: CAiSE 2022. LNCS, vol. 13295, pp. 339–354. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07472-1_20
Klijn, E.L., Mannhardt, F., Fahland, D.: Classifying and detecting task executions and routines in processes using event graphs. In: BPM 2021. LNBIP, vol. 427, pp. 212–229. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85440-9_13
Klijn, E.L., Mannhardt, F., Fahland, D.: Aggregating event knowledge graphs for task analysis. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) ICPM 2022. LNBIP, vol. 468, pp. 493–505. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_36
Kremser, W., Blagoev, B.: The dynamics of prioritizing: how actors temporally pattern complex role-routine ecologies. Adm. Sci. Q. 66(2), 339–379 (2021)
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346–2363 (2018)
Pentland, B., Feldman, M., Becker, M., Liu, P.: Dynamics of organizational routines: a generative model. J. Manag. Stud. 49, 1484–1508 (2012)
Sato, D.M.V., Freitas, S.C.D., Barddal, J.P., Scalabrin, E.E.: A survey on concept drift in process mining. ACM Comput. Surv. 54(9), 189:1–189:38 (2022)
Tour, A., Polyvyanyy, A., Kalenkova, A.A.: Agent system mining: vision, benefits, and challenges. IEEE Access 9, 99480–99494 (2021)
Wambui, G.D., Waititu, G.A., Wanjoya, A.K.: The power of the pruned exact linear time (PELT) test in multiple changepoint detection. AJTAS 4, 581–586 (2015)
Wurm, B., Grisold, T., Mendling, J., vom Brocke, J.: Business process management and routine dynamics, pp. 513–524. Cambridge University Press (2021)
Yang, J., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M., Yu, Y.: OrdinoR: a framework for discovering, evaluating, and analyzing organizational models using event logs. Decis. Support Syst. 158, 113771 (2022)
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Klijn, E.L., Mannhardt, F., Fahland, D. (2024). Multi-perspective Concept Drift Detection: Including the Actor Perspective. In: Guizzardi, G., Santoro, F., Mouratidis, H., Soffer, P. (eds) Advanced Information Systems Engineering. CAiSE 2024. Lecture Notes in Computer Science, vol 14663. Springer, Cham. https://doi.org/10.1007/978-3-031-61057-8_9
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