Abstract
Process mining practices are mainly activity-oriented and they seldom consider the (often conflicting) goals of stakeholders. Involving goal-related factors, as often done in requirements engineering, can improve the rationality and interpretability of mined models and lead to better opportunities to satisfy stakeholders. This paper proposes a new Goal-oriented Process Enhancement and Discovery (GoPED) method to align discovered models with stakeholders’ goals. GoPED first adds goal-related attributes to traditional event characteristics (case identifier, activities, and timestamps), selects a subset of cases with respect to a goal-related criterion, and finally discovers a process model from that subset. We define three types of criteria that suggest desired satisfaction levels from a (i) case perspective, (ii) goal perspective, and (iii) organization perspective. For each criterion, an algorithm is proposed to enable selecting the best subset of cases were the criterion holds. The resulting process models are expected to reproduce the desired level of satisfaction. A synthetic event log is used to illustrate the proposed algorithms and to discuss their results.
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References
Amyot, D., Ghanavati, S., Horkoff, J., Mussbacher, G., Peyton, L., Yu, E.: Evaluating goal models within the goal-oriented requirement language. Int. J. Intell. Syst. 25(8), 841–877 (2010). https://doi.org/10.1002/int.20433
Amyot, D., Mussbacher, G.: User requirements notation: the first ten years, the next ten years. J. Softw. 6(5), 747–768 (2011)
Bernard, B., Andritsos, P.: CJM-ex: goal-oriented exploration of customer journey maps using event logs and data analytics. In: BPM Demo Track and BPM Dissertation Award (BPMD&DA), vol. 1920. EUR-WS (2017)
Fan, Y., Anda, A.A., Amyot, D.: An arithmetic semantics for GRL goal models with function generation. In: Khendek, F., Gotzhein, R. (eds.) SAM 2018. LNCS, vol. 11150, pp. 144–162. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01042-3_9
Ghasemi, M.: Towards goal-oriented process mining. In: 2018 IEEE 26th International Requirements Engineering Conference (RE), pp. 484–489. IEEE CS (2018). https://doi.org/10.1109/re.2018.00066
Ghasemi, M.: What requirements engineering can learn from process mining. In: 2018 1st International Workshop on Learning from other Disciplines for Requirements Engineering (D4RE), pp. 8–11. IEEE (2018). https://doi.org/10.1109/d4re.2018.00008
Ghasemi, M., Amyot, D.: From event logs to goals: a systematic literature review of goal-oriented process mining. Requir. Eng. 1–27 (2019). https://doi.org/10.1007/s00766-018-00308-3
Horita, H., Hirayama, H., Tahara, Y., Ohsuga, A.: Towards goal-oriented conformance checking. In: Proceedings of the International Conference on Software Engineering and Knowledge Engineering SEKE, pp. 722–724 (2015)
jUCMNav (2016). http://softwareengineering.ca/jucmnav
Mannhardt, F., de Leoni, M., Reijers, H., van der Aalst, W., Toussaint, P.: Guided process discovery – a pattern-based approach. Inf. Syst. 76, 1–18 (2018). https://doi.org/10.1016/j.is.2018.01.009
Outmazgin, N., Soffer, P.: A process mining-based analysis of business process work-arounds. Softw. Syst. Model. 15(2), 309–323 (2016)
Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006). https://doi.org/10.1007/11837862_18
Ponnalagu, K., Ghose, A., Narendra, Nanjangud C., Dam, H.K.: Goal-aligned categorization of instance variants in knowledge-intensive processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 350–364. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_24
Santiputri, M., Deb, N., Khan, M.A., Ghose, A., Dam, H., Chaki, N.: Mining goal refinement patterns: distilling know-how from data. In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. (eds.) ER 2017. LNCS, vol. 10650, pp. 69–76. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69904-2_6
Seeliger, A., Nolle, T., Mühlhäuser, M.: Finding structure in the unstructured: hybrid feature set clustering for process discovery. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 288–304. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_17
van der Aalst, W.: Process Mining Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Wen, L., van der Aalst, W., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Disc. 15(2), 145–180 (2007)
Yan, J., Hu, D., Liao, S., Wang, H.: Mining agents’ goals in agent-oriented business processes. ACM Trans. Manag. Inf. Syst. 5(4), 1–22 (2014)
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This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC, Discovery and CGS-D).
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Ghasemi, M., Amyot, D. (2019). Goal-oriented Process Enhancement and Discovery. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds) Business Process Management. BPM 2019. Lecture Notes in Computer Science(), vol 11675. Springer, Cham. https://doi.org/10.1007/978-3-030-26619-6_9
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