Abstract
The problem of automated discovery of process models from event logs has been intensely investigated in the past two decades, leading to a range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by using metaheuristic optimization. However, these studies have remained at the level of proposals without validation on real-life logs or they have only considered one metaheuristics in isolation. In this setting, this paper studies the following question: To what extent can the accuracy of automated process discovery approaches be improved by applying different optimization metaheuristics? To address this question, the paper proposes an approach to enhance automated process discovery approaches with metaheuristic optimization. The approach is instantiated to define an extension of a state-of-the-art automated process discovery approach, namely Split Miner. The paper compares the accuracy gains yielded by four optimization metaheuristics relative to each other and relative to state-of-the-art baselines, on a benchmark comprising 20 real-life logs. The results show that metaheuristic optimization improves the accuracy of Split Miner in a majority of cases, at the cost of execution times in the order of minutes, versus seconds for the base algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
\(\theta \) maps each node of the DFG to a natural number.
- 2.
Herein, when using the term DFG, we refer to the processed DFG (after filtering).
- 3.
Split Miner has two hyperparameters: the noise filtering threshold, used to drop infrequent edges in the DFG, and the parallelism threshold, used to determine which potential parallel relations between activities are used when discovering the process model from the DFG.
- 4.
Available under the label “Metaheuristically Optimized Split Miner” at http://apromore.org/platform/tools.
- 5.
https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f, https://doi.org/10.4121/uuid:a7ce5c55-03a7-4583-b855-98b86e1a2b07, https://doi.org/10.4121/uuid:c3e5d162-0cfd-4bb0-bd82-af5268819c35, https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1, https://doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b.
- 6.
- 7.
References
Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B., van der Aalst, W.: Measuring precision of modeled behavior. ISeB 13(1), 37–67 (2015)
Adriansyah, A., van Dongen, B., van der Aalst, W.: Conformance checking using cost-based fitness analysis. In: EDOC. IEEE (2011)
Alizadeh, S., Norani, A.: ICMA: a new efficient algorithm for process model discovery. Appl. Intell. 48(11), 4497–4514 (2018)
Augusto, A., Armas-Cervantes, A., Conforti, R., Dumas, M., La Rosa, M., Reissner, D.: Abstract-and-compare: a family of scalable precision measures for automated process discovery. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 158–175. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_10
Augusto, A., Armas Cervantes, A., Conforti, R., Dumas, M., La Rosa, M., Reissner, D.: Measuring fitness and precision of automatically discovered process models: a principled and scalable approach. Technical report, University of Melbourne (2019)
Augusto, A., Conforti, R., Dumas, M., La Rosa, M., Bruno, G.: Automated discovery of structured process models from event logs: the discover-and-structure approach. DKE 117, 373–392 (2017)
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. KAIS 59, 251–284 (2018)
Boussaïd, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)
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.: Automatic determination of parameters’ values for heuristics miner++. In: IEEE Congress on Evolutionary Computation (2010)
Chifu, V.R., Pop, C.B., Salomie, I., Balla, I., Paven, R.: Hybrid particle swarm optimization method for process mining. In: ICCP. IEEE (2012)
Conforti, R., La Rosa, M., ter Hofstede, A.: Filtering out infrequent behavior from business process event logs. IEEE TKDE 29(2), 300–314 (2017)
de Medeiros, A.K.A.: Genetic process mining. Ph.D. thesis, Eindhoven University of Technology (2006)
Gao, D., Liu, Q.: An improved simulated annealing algorithm for process mining. In: CSCWD. IEEE (2009)
Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
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., Fahland, D., van der Aalst, W.: Scalable process discovery and conformance checking. Softw. Syst. Model. 17, 599–631 (2016)
Ribeiro, J., Carmona Vargas, J.: A method for assessing parameter impact on control-flow discovery algorithms. In: Algorithms and Theories for the Analysis of Event Data (2015)
Song, W., Liu, S., Liu, Q.: Business process mining based on simulated annealing. In: ICYCS. IEEE (2008)
Stützle, T.: Local search algorithms for combinatorial problems. Ph.D. thesis, Darmstadt University of Technology (1998)
van der Aalst, W.: Process Mining - Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
vanden Broucke, S., De Weerdt, J.: Fodina: a robust and flexible heuristic process discovery technique. DSS 100, 109–118 (2017)
Weijters, A., Ribeiro, J.: Flexible heuristics miner (FHM). In: CIDM. IEEE (2011)
Acknowledgements
We thank Raffaele Conforti for his input to an earlier version of this paper. This research is partly funded by the Australian Research Council (DP180102839) and the Estonian Research Council (IUT20-55).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Augusto, A., Dumas, M., La Rosa, M. (2019). Metaheuristic Optimization for Automated Business Process 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_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-26619-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26618-9
Online ISBN: 978-3-030-26619-6
eBook Packages: Computer ScienceComputer Science (R0)