Abstract
The use of immense amounts of data on the execution of applications based on business processes can make it possible, thanks to Process Mining, to detect trends. Indeed, human intelligence in decision-making is enriched by Machine Learning in order to avoid bottlenecks, improve efficiency and highlight potential process improvements. In this research article, we present a method (BPETPM) for predictive monitoring of business processes. This method allows to predict the execution time of a business process according to the path followed by the process instance. It predicts whether a process instance will run in time or late. We follow the CRISP-DM approach, known in Data Science, to carry out our method. The input data for learning is extracted from the event logs saving the execution traces of the workflow engine of a BPMS. We start by cleaning data, adding additional attributes, and encoding categorical variables. Then, at the modelling level, we test six classification algorithms : KNN, SVM(kernel=linear), SVM(kernel=rbf), Decision Tree, Random Forest and Logestic Regression. Then, using the BPETPM method, we create an intelligent process management system (iBPMS4PET). This system is applied to a process for managing incoming mail in the mutual health sector.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Van der Aalst, W., Pesic, M., Song, M.: Beyond Process Mining: From the Past to Present and Future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38–52. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13094-6_5
Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D.: Completion time and next activity prediction of processes using sequential pattern mining. DS, pp. 49–61, (2014)
Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of business process management. Second Edition, (2018). https://doi.org/10.1007/978-3-662-56509-4
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
Carmona, J., Cortadella, J., Kishinevsky, M.: A Region-Based Algorithm for Discovering Petri Nets from Event Logs. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 358–373. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85758-7_26
Van der Aalst, W., Adriansyah, A.: Replaying History on Process Models for Conformance Checking and Performance Analysis. Article in Wiley Interdisciplinary Reviews Data Min. Knowl. Discov. 2, 182-192 (2012)
Van der Aalst, W.: Process Mining: data Science in Action. Second Edition. Springer Heidelberg New York Dordrecht London, pp. 85-88, (2016). https://doi.org/10.1007/978-3-662-49851-4
Van der Aalst, W., Bichler, M., Heinzl, A.: Robotic Process Automation. Bus. Inf. Syst. Eng. 60(4), 269–272 (2018). https://doi.org/10.1007/s12599-018-0542-4
Teinemaa, I., Dumas, M., La Rosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17 (2019)
Van der Aalst, W., Schonenberg, H, Song, M.: Time prediction based on process mining. Ulsan National Institute of Science and Technology, 100 Banyeon-RI, Ulsan, South Korea, pp. 689-798, (2011)
Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D.: Completion time and next activity prediction of processes using sequential pattern mining. DS, pp. 49–61, (2014)
Folino, F., Greco, G., Guzzo, A., Pontieri, L.: Mining usage scenarios in business processes: outlieraware discovery and run-time prediction. Data Knowl. Eng. 70(12), 1005–1029 (2011)
Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. Confederated International Conferences : CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, September 10-14, 2012 In: Proceedings, Part I, Vol. 7565 of Lecture Notes in Computer Science, Springer, pp. 287-304, (2012). https://doi.org/10.1007/978-3-642-33606-5_18
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Data-Aware Remaining Time Prediction of Business Process Instances. In: IJCNN (WCCI), (2014)
de Leoni, M., van der Aalst, W., Dees, M.: A General Framework for Correlating Business Process Characteristics. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 250–266. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10172-9_16
Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: A markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42(1), 97–126 (2013). https://doi.org/10.1007/s10115-013-0697-8
Ghattas, J., Soffer, P., Peleg, M.: Improving business process decision making based on past experience. Decis. Support Syst. 59, pp. 93-107, (2014)
Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining for delay prediction in multiclass service processes. Inf. Syst. 53, 278–295 (2015)
Rogge-Solti, A., Weske, M.: Prediction of business process durations using non-markovian stochastic petri nets. Inf. Syst. 54, 1–14 (2015)
Maggi, FM., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke M et al (eds) CAiSe proceedings, Thessaloniki, pp. 457–472, (2014)
Tax, N., Verenich, MLR., Dumas, M.: Predictive Business Process Monitoring with LSTM Neural Networks. In CAiSE, (2017)
Kratsch, W., Manderscheid, J., Röglinger, M.: Machine Learning in Business Process Monitoring: a Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction. Bus. Inf. Syst. Eng. 63(3), 261–276 (2020)
Zhang, Y.: Sales Forecasting of Promotion Activities Based on the Cross-Industry Standard Process for Data Mining of E-commerce Promotional Information and Support Vector Regression. J. Comput. 32(1), 212–225 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ben Fradj, W., Turki, M. (2023). Prediction of Business Process Execution Time. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-35507-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35506-6
Online ISBN: 978-3-031-35507-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)