Abstract
Enterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log – an operation known as automated process discovery. Despite a rich body of research in the field, existing automated process discovery techniques do not fully capture the concurrency inherent in a business process. Specifically, the bulk of these techniques treat two activities A and B as concurrent if sometimes A completes before B and other times B completes before A. Typically though, activities in a business process are executed in a true concurrency setting, meaning that two or more activity executions overlap temporally. This paper addresses this gap by presenting a refined version of an automated process discovery technique, namely Split Miner, that discovers true concurrency relations from event logs containing start and end timestamps for each activity. The proposed technique is also able to differentiate between exclusive and inclusive choices. We evaluate the proposed technique relative to existing baselines using 11 real-life logs drawn from different industries.
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Notes
- 1.
For simplicity, we use the term activity to refer to its label.
- 2.
The frequency of a directly-follows relation is the number of times the relation is observed.
- 3.
E.g. including start and end states.
- 4.
With at least one observation of mutual exclusiveness every two observations of concurrency or vice-versa.
- 5.
- 6.
Available as “Split Miner 2.0” at http://apromore.org/platform/tools.
References
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P.: Alignment based precision checking. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_15
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B., van der Aalst, W.M.P.: Measuring precision of modeled behavior. ISeB 13(1), 37–67 (2015)
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Augusto, A., Armas Cervantes, A., Conforti, R., Dumas, M., Rosa, L.: Measuring fitness and precision of automatically discovered process models: a principled and scalable approach. IEEE TKDE (2020, to appear)
Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE TKDE 31(4), 686–705 (2019)
Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Polyvyanyy, A.: Split miner: automated discovery of accurate and simple business process models from event logs. Knowl. Inf. Syst. 59(2), 251–284 (2018). https://doi.org/10.1007/s10115-018-1214-x
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33606-5_19
Burattin, A., Sperduti, A.: Heuristics miner for time intervals. In: ESANN (2010)
Leemans, S.J.J., Fahland, D.: Information-preserving abstractions of event data in process mining. Knowl. Inf. Syst. 62(3), 1143–1197 (2019). https://doi.org/10.1007/s10115-019-01376-9
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_6
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Using life cycle information in process discovery. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 204–217. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_17
Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. LNBIP, vol. 6. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89224-3
Schimm, G.: Mining exact models of concurrent workflows. Comput. Ind. 53(3), 265–281 (2004)
Senderovich, A., Weidlich, M., Gal, A.: Temporal network representation of event logs for improved performance modelling in business processes. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 3–21. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_1
Syring, A.F., Tax, N., van der Aalst, W.M.P.: Evaluating conformance measures in process mining using conformance propositions. In: Koutny, M., Pomello, L., Kristensen, L.M. (eds.) Transactions on Petri Nets and Other Models of Concurrency XIV. LNCS, vol. 11790, pp. 192–221. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-662-60651-3_8
van der Aalst, W.M.P.: Process Mining - Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
van der Aalst, W.M.P., et al.: Soundness of workflow nets: classification, decidability, and analysis. Formal Asp. Comput. 23(3), 333–363 (2011)
van der Werf, J.M.E.M., Mans, R., van der Aalst, W.M.P.: Mining declarative models using time intervals. In: PNSE+ ModPE, pp. 313–331. Citeseer (2013)
Wen, L., Wang, J., van der Aalst, W.M.P., Huang, B., Sun, J.: A novel approach for process mining based on event types. J. Intell. Inf. Syst. 32(2), 163–190 (2009)
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Research funded by the Australian Research Council (grant DP180102839) and the Estonian Research Council (grant PRG887).
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Augusto, A., Dumas, M., La Rosa, M. (2021). Automated Discovery of Process Models with True Concurrency and Inclusive Choices. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_4
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