Abstract
Process mining is one of the research disciplines belonging to the field of Business Process Management (BPM). The central idea of process mining is to use real process execution logs in order to discover, model, and improve business processes. There are multiple approaches to modeling processes with the most prevalent one being the procedural models like Petri nets and BPMN models. However, procedural models can be difficult to use for processes like software processes that are highly variable and can have a high number of different branches and exceptions. In these cases, it may be better to use declarative models, because declarative models do not aim to model the end-to-end process step by step, but they constrain the behavior of the process using rules thus allowing for more flexibility in the process executions. The goal of this paper is to introduce the main principles of declarative process mining (i.e., process mining based on declarative models) and to show which state-of-the-art declarative process mining techniques have been implemented in the RuM toolkit and in the Declare4Py Python library.
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Notes
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International Organization for Standardization, ISO quality management principles, 2015: https://www.iso.org/files/live/sites/isoorg/files/store/en/PUB100080.pdf.
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Branched Declare is an extension of Declare in which constraints are defined over disjunctions of activities.
References
van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19
van der Aalst, W.M.P., de Beer, H.T., van Dongen, B.F.: Process mining and verification of properties: an approach based on temporal logic. In: Meersman, R., Tari, Z. (eds.) OTM 2005. LNCS, vol. 3760, pp. 130–147. Springer, Heidelberg (2005). https://doi.org/10.1007/11575771_11
Alman, A., Di Ciccio, C., Haas, D., Maggi, F.M., Nolte, A.: Rule mining with RuM. In: ICPM, pp. 121–128 (2020)
Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE Trans. Knowl. Data Eng. 31(4), 686–705 (2019)
Bergami, G., Maggi, F.M., Marrella, A., Montali, M.: Aligning data-aware declarative process models and event logs. In: BPM, vol. 12875, pp. 235–251 (2021)
Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016)
Chiariello, F., Maggi, F.M., Patrizi, F.: ASP-based declarative process mining. In: AAAI, pp. 5539–5547. AAAI Press (2022)
De Giacomo, G., De Masellis, R., Maggi, F.M., Montali, M.: Monitoring constraints and metaconstraints with temporal logics on finite traces. ACM Trans. Softw. Eng. Methodol. 31(4), 68:1–68:44 (2022)
De Giacomo, G., Maggi, F.M., Marrella, A., Patrizi, F.: On the disruptive effectiveness of automated planning for LTLf-based trace alignment. In: AAAI, pp. 3555–3561. AAAI Press (2017)
De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: IJCAI, pp. 854–860 (2013)
Di Ciccio, C., Bernardi, M.L., Cimitile, M., Maggi, F.M.: Generating event logs through the simulation of declare models. In: Barjis, J., Pergl, R., Babkin, E. (eds.) EOMAS 2015. LNBIP, vol. 231, pp. 20–36. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24626-0_2
Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM Trans. Manag. Inf. Syst. 5(4), 24:1–24:37 (2015)
Donadello, I., Riva, F., Maggi, F.M., Shikhizada, A.: Declare4Py: a python library for declarative process mining. In: BPM Demos. CEUR Workshop Proceedings, vol. 3216, pp. 117–121 (2022)
Gunther, C.W., Verbeek, H.: XES-standard definition (2014)
Leno, V., Dumas, M., Maggi, F.M.: Correlating activation and target conditions in data-aware declarative process discovery. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 176–193. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_11
Maggi, F.M., Di Ciccio, C., Di Francescomarino, C., Kala, T.: Parallel algorithms for the automated discovery of declarative process models. Inf. Syst. 74(Part), 136–152 (2018)
Maggi, F.M., Dumas, M., García-Ba nuelos, L., Montali, M.: Discovering data-aware declarative process models from event logs. In: BPM, vol. 8094, pp. 81–96 (2013)
Maggi, F.M., Marrella, A., Patrizi, F., Skydanienko, V.: Data-aware declarative process mining with SAT. ACM Trans. Intell. Syst. Technol. (2023). https://doi.org/10.1145/3600106
Maggi, F.M., Montali, M., Bhat, U.: Compliance monitoring of multi-perspective declarative process models. In: EDOC, pp. 151–160 (2019)
Maggi, F.M., Montali, M., Westergaard, M., van der Aalst, W.M.P.: Monitoring business constraints with linear temporal logic: an approach based on colored automata. In: BPM, vol. 6896, pp. 132–147 (2011)
Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC), pp. 287–300 (2007)
Schunselaar, D.M.M., Maggi, F.M., Sidorova, N.: Patterns for a log-based strengthening of declarative compliance models. In: IFM, vol. 7321, pp. 327–342 (2012)
Skydanienko, V., Di Francescomarino, C., Ghidini, C., Maggi, F.M.: A tool for generating event logs from multi-perspective declare models. In: BPM (Dissertation/Demos/Industry). CEUR Workshop Proceedings, vol. 2196, pp. 111–115. CEUR-WS.org (2018)
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Alman, A., Donadello, I., Maggi, F.M., Montali, M. (2024). Declarative Process Mining for Software Processes: The RuM Toolkit and the Declare4Py Python Library. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V., Li, X. (eds) Product-Focused Software Process Improvement. PROFES 2023. Lecture Notes in Computer Science, vol 14484. Springer, Cham. https://doi.org/10.1007/978-3-031-49269-3_2
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