Abstract
Predicting the next event(s) in Business Processes is becoming more important as more and more systems are getting automated. Predicting deviating behaviour early on in a process can ensure that possible errors are identified and corrected or that unwanted delays are avoided. We propose to use Bayesian Networks to capture dependencies between the attributes in a log to obtain a fine-grained prediction of the next activity. Elaborate comparisons show that our model performs at par with the state-of-the-art methods. Our model, however, has the additional benefit of explainability; due to its underlying Bayesian Network, it is capable of providing a comprehensible explanation of why a prediction is made. Furthermore, the runtimes of our learning algorithm are orders of magnitude lower than those state-of-the-art methods that are based on deep neural networks.
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Acknowledgments
The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI.
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Pauwels, S., Calders, T. (2020). Bayesian Network Based Predictions of Business Processes. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management Forum. BPM 2020. Lecture Notes in Business Information Processing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-58638-6_10
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DOI: https://doi.org/10.1007/978-3-030-58638-6_10
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