Predictive Business Process Monitoring with tree-based classification algorithms

Tomasz Owczarek, Piotr Janke

Abstract


Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that


Keywords


business process, prediction, classification, random forest, gradient boosting

Full Text:

PDF

References


Breiman L., 2001. Random forests. Machine learning, 45, 5-32.

Chen T., Guestrin C., 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM International Conference On Knowledge Discovery And Data Mining, 785-794.

Evermann, J., Rehse, J. R., Fettke, P., 2016, A deep learning approach for predicting process behaviour at runtime. International Conference on Business Process Management, Springer, Cham, 327-338.

Fernández-Delgado M., Cernadas E., Barro S., Amorim D., 2014. Do we need hundreds of classifiers to solve real world classification problems. Journal of Machine Learning Research, 15, 3133-3181.

Folinas D., Bochtis D., Sorensen C., 2010. In-field logistics processes management based on business activities monitoring systems paradigm. International Journal of Logistics Systems and Management, 8, 1–18.

Folino, F., Guarascio, M., Pontieri, L., 2012. Discovering context-aware models for predicting business process performances. In: Meersman, R., Panetto, H., Dillon, T.,Rinderle-Ma, S., Dadam, P., Zhou, X., Pearson, S., Ferscha, A., Bergamaschi, S., Cruz, I.F. (eds.) OTM 2012, Part I. LNCS, vol. 7565, Springer, Heidelberg, 287–304.

Friedman J., Hastie T., Tibshirani R., 2009. The elements of statistical learning. New York: Springer series in statistics.

Hammer M., Champy J., 1996. Reengineering w przedsiębiorstwie. Neumann Management Institute, Warszawa.

Jansen-Vullers M.H., Netjes M. Business Process Simulation - A Tool Survey. Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, 38, Aarhus, Denmark, 1-20.

Kang, B., Kim, D., Kang, S., 2012. Real-time business process monitoring method for prediction of abnormal termination using knni-based LOF prediction. Expert Syst. Appl. 39, 6061-6068.

Kaplan R. S., David P. Norton, 1992. The Balanced Scorecard – Measures that Drive Performance. Harvard Business Review, 71-79.

Kuhn M., Johnson K., 2013. Applied predictive modeling. New York: Springer.

Leontjeva, A., Conforti, R., Francescomarino, C.D., Dumas, M., Maggi, F.M., 2015. Complex symbolic sequence encodings for predictive monitoring of business processes. Business Process Management - 13th International Conference, BPM 2015, Innsbruck, Austria, 297-313.

Maggi, F. M., Di Francescomarino, C., Dumas, M., Ghidini, C., 2014. Predictive monitoring of business processes, International Conference on Advanced Information Systems Engineering, Springer, Cham, 457-472.

Metzger A., Leitner P., Ivanović D., Schmieders E., Franklin R., Carro M., Dustdar S., Pohl, K., 2015. Comparing and combining predictive business process monitoring techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45, 276-290.

Nowakowski, T., Werbinska-Wojciechowska, S., Chlebus, M.,2015. Supply Chain Vulnerability Assessment Methods–Possibilities and Limitations. Safety and Reliability of Complex Engineered Systems, Taylor & Francis Group, London,1667-1678.

Ogutu J. O., Piepho H-P., Schulz-Streeck T., 2011. A comparison of random forests, boosting and support vector machines for genomic selection. BMC proceedings, 5, BioMed Central, 2011, available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103196/

Owczarek T., 2017, An Example of Exploratory Analysis for Predictive Business Process Monitoring. Proceedings of the 30th International Business Information Management Association Conference, Madrid, Spain, 4224-4228.

Pradabwong, J., Braziotis, C., Tannock, J., & Pawar, K. S., 2017. Business process management and supply chain collaboration: effects on performance and competitiveness. Supply Chain Management: An International Journal, 22(2).

Rosemann M., vom Brocke J., 2015. The Six Core Elements of Business Process Management. Handbook on business process management, vol 2., Springer Heidelberg, 105-122.

Ruzevicius J., Miškele M., Darius K., 2012. Peculiarities of The Business Process Management Lifecycle at Different Maturity Levels: The Banking Sector’s Case. Issues of Business and Law, 4, 2012.

Teinemaa, I., Dumas, M., Maggi, F. M., Di Francescomarino, C., 2016. Predictive business process monitoring with structured and unstructured data. International Conference on Business Process Management , Springer International Publishing, 401-417.

van der Aalst W. M. P., M. H. Schonenberg, Song M., 2011., Time prediction based on process mining. Information Systems, 36, 450–475.

Wainberg M., Alipanahi B., Frey B. J., 2016. Are random forests truly the best classifiers? The Journal of Machine Learning Research, 17, 3837-3841.


Refbacks

  • There are currently no refbacks.