Abstract
Many of today’s information systems record the execution of (business) processes in great detail. Process mining utilizes such data and aims to extract valuable insights. Process discovery, a key research area in process mining, deals with the construction of process models based on recorded process behavior. Existing process discovery algorithms aim to provide a “push-button-technology”, i.e., the algorithms discover a process model in a completely automated fashion. However, real data often contain noisy and/or infrequent complex behavioral patterns. As a result, the incorporation of all behavior leads to very imprecise or overly complex process models. At the same time, data pre-processing techniques have shown to be able to improve the precision of process models, i.e., without explicitly using domain knowledge. Yet, to obtain superior process discovery results, human input is still required. Therefore, we propose a discovery algorithm that allows a user to incrementally extend a process model by new behavior. The proposed algorithm is designed to localize and repair nonconforming process model parts by exploiting the hierarchical structure of the given process model. The evaluation shows that the process models obtained with our algorithm, which allows for incremental extension of a process model, have, in many cases, superior characteristics in comparison to process models obtained by using existing process discovery and model repair techniques.
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Notes
- 1.
For example, the Inductive Miner algorithm [16] fulfills the listed requirements.
References
van der Aalst, W.M.P.: The application of petri nets to workflow management. J. Circ. Syst. Comput. 8(1), 21–66 (1998). https://doi.org/10.1142/S0218126698000043
van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.F.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(2), 182–192 (2012). https://doi.org/10.1002/widm.1045
van der Aalst, W.M.P.: On the representational bias in process mining. In: Reddy, S., Tata, S. (eds.) Proceedings of the 20th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, WETICE 2011, Paris, France, 27–29 June 2011, pp. 2–7. IEEE Computer Society (2011). https://doi.org/10.1109/WETICE.2011.64
van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Adriansyah, A.: Aligning Observed and Modeled Behavior. Ph.D. thesis, Eindhoven University of Technology, Department of Mathematics and Computer Science, July 2014. https://doi.org/10.6100/IR770080
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Measuring precision of modeled behavior. Inf. Syst. E-Bus. Manag. 13(1), 37–67 (2015). https://doi.org/10.1007/s10257-014-0234-7
Armas Cervantes, A., van Beest, N.R.T.P., La Rosa, M., Dumas, M., García-Bañuelos, L.: Interactive and incremental business process model repair. In: Panetto, H., Debruyne, C., Gaaloul, W., Papazoglou, M., Paschke, A., Ardagna, C.A., Meersman, R. (eds.) OTM 2017. LNCS, vol. 10573, pp. 53–74. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_5
Berti, A., Zelstvan Zelst, S.J., Aalstvan der Aalst, W.M.P.: Process mining for python (PM4Py): bridging the gap between process-and data science. In: Proceedings of the ICPM Demo Track 2019, Co-Located with 1st International Conference on Process Mining (ICPM 2019), Aachen, Germany, 24–26 June 2019, pp. 13–16 (2019). http://ceur-ws.org/Vol-2374/
Dixit, P.: Interactive process mining. Ph.D. thesis, Department of Mathematics and Computer Science, June 2019
Dixit, P.M., Verbeek, H.M.W., Buijs, J.C.A.M., van der Aalst, W.M.P.: Interactive data-driven process model construction. In: Trujillo, J.C., Davis, K.C., Du, X., Li, Z., Ling, T.W., Li, G., Lee, M.L. (eds.) ER 2018. LNCS, vol. 11157, pp. 251–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00847-5_19
van Dongen, B.F., Alves de Medeiros, A.K., Wen, L.: Process mining: overview and outlook of petri net discovery algorithms. In: Jensen, K., van der Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 225–242. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00899-3_13
Fahland, D., van der Aalst, W.M.P.: Repairing process models to reflect reality. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 229–245. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32885-5_19
Fahland, D., van der Aalst, W.M.P.: Model repair: aligning process models to reality. Inf. Syst. 47, 220–243 (2015). https://doi.org/10.1016/j.is.2013.12.007
Kalsing, A., do Nascimento, G.S., Iochpe, C., Thom, L.H.: An incremental process mining approach to extract knowledge from legacy systems. In: Proceedings of the 14th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2010, Vitória, Brazil, 25–29 October 2010, pp. 79–88. IEEE Computer Society (2010). https://doi.org/10.1109/EDOC.2010.13
Kindler, E., Rubin, V., Schäfer, W.: Incremental workflow mining based on document versioning information. In: Li, M., Boehm, B., Osterweil, L.J. (eds.) SPW 2005. LNCS, vol. 3840, pp. 287–301. Springer, Heidelberg (2006). https://doi.org/10.1007/11608035_25
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
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). https://doi.org/10.1007/s10270-016-0545-x
Leonide Leoni, M., Mannhardt, F.: Road traffic fine management process - event log. 4TU.Centre for Research Data (2015), https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5. Accessed 12 Oct 2019
Sun, W., Li, T., Peng, W., Sun, T.: Incremental workflow mining with optional patterns and its application to production printing process. Int. J. Intell. Control Syst. 12, 45–55 (2007)
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Schuster, D., van Zelst, S.J., van der Aalst, W.M.P. (2020). Incremental Discovery of Hierarchical Process Models. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_25
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