Abstract
Predictive business process monitoring is concerned with the prediction how a running process instance will unfold up to its completion at runtime. Most of the proposed approaches rely on a wide number of machine learning techniques. In the last years numerous studies revealed that these methods can be successfully applied for different prediction targets. However, these techniques require a qualitatively and quantitatively sufficient dataset. Unfortunately, there are many situations in business process management where only a quantitatively insufficient dataset is available. The problem of insufficient data in the context of BPM is still neglected. Hence, none of the comparative studies investigates the performance of predictive business process monitoring techniques in environments with small datasets. In this paper an evaluation framework for comparing existing approaches with regard to their suitability for small datasets is developed and exemplarily applied to state-of-the-art approaches in next activity prediction.
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
Notes
- 1.
At this point we implicitly assume that the event logs currently used in research can be considered as quantitatively sufficient.
- 2.
- 3.
i.e., it can be calculated from true positives (TP), true negatives (TN), false positives (FP), and true positive (TP).
- 4.
References
Breuker, D., Matzner, M., Delfmann, P., Becker, J.: Comprehensible predictive models for business processes. MIS Q. 40(4), 1009–1034 (2016)
Camargo, M., Dumas, M., González-Rojas, O.: Learning accurate LSTM models of business processes. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 286–302. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_19
Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P.: Supporting risk-informed decisions during business process execution. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 116–132. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_8
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27
Di Mauro, N., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019. LNCS (LNAI), vol. 11946, pp. 348–361. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35166-3_25
Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Supp. Syst. 100, 129–140 (2017)
Hinkka, M., Lehto, T., Heljanko, K.: Exploiting event log event attributes in RNN based prediction. In: Welzer, T., et al. (eds.) ADBIS 2019. CCIS, vol. 1064, pp. 405–416. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30278-8_40
Käppel, M., Schönig, S., Jablonski, S.: Leveraging small sample learning for business process management. Inf. Softw. Technol. 132, 106472 (2020)
Kratsch, W., Manderscheid, J., Röglinger, M., Seyfried, J.: Machine learning in business process monitoring: a comparison of deep learning and classical approaches used for outcome prediction. BISE 63, 261–271 (2020). https://doi.org/10.1007/s12599-020-00645-0
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
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31
Metzger, A., et al.: Comparing and combining predictive business process monitoring techniques. IEEE Trans. Syst. Man Cybern. 45(2), 276–290 (2015)
Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: Proceedings of ICPM 2019 (2019)
Rama-Maneiro, E., Vidal, J., Lama, M.: Deep learning for predictive business process monitoring: review and benchmark. ArXiv arXiv:2009.13251 (2020)
Rogge-Solti, A., Weske, M.: Prediction of business process durations using non-Markovian stochastic petri nets. Inf. Syst. 54, 1–14 (2015)
Rozinat, A., de Medeiros, A.K.A., Günther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The need for a process mining evaluation framework in research and practice. In: ter Hofstede, A., Benatallah, B., Paik, H.-Y. (eds.) BPM 2007. LNCS, vol. 4928, pp. 84–89. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78238-4_10
Schönig, S., Jasinski, R., Ackermann, L., Jablonski, S.: Deep learning process prediction with discrete and continuous data features. In: Proceedings of ENASE 2018 (2018)
Shu, J., Xu, Z., Meng, D.: Small sample learning in big data era. CoRR abs/1808.04572 (2018). arXiv:1808.04572
Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477–492. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_30
Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I.: Predictive business process monitoring via generative adversarial nets: the case of next event prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 237–256. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_14
Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. TKDD 13(2), 1–57 (2019)
Unuvar, M., Lakshmanan, G.T., Doganata, Y.N.: Leveraging path information to generate predictions for parallel business processes. Knowl. Inf. Syst. 47(2), 433–461 (2015). https://doi.org/10.1007/s10115-015-0842-7
Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM TIST 10(4), 1–34 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Käppel, M., Jablonski, S., Schönig, S. (2021). Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs. In: Paiva, A.C.R., Cavalli, A.R., Ventura Martins, P., Pérez-Castillo, R. (eds) Quality of Information and Communications Technology. QUATIC 2021. Communications in Computer and Information Science, vol 1439. Springer, Cham. https://doi.org/10.1007/978-3-030-85347-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-85347-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85346-4
Online ISBN: 978-3-030-85347-1
eBook Packages: Computer ScienceComputer Science (R0)