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Title
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Bayesian network based predictions of business processes
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Author
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Abstract
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| 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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Language
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English
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Source (book)
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Business Process Management (BPM) Forum 2020, September 13–18, Seville, Spain
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Source (series)
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Lecture Notes in Business Information Processing (LNBIP) ; 392
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Publication
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Cham
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Springer
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2020
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ISBN
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978-3-030-58637-9
[print]
978-3-030-58638-6
[online]
978-3-030-58638-6
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DOI
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10.1007/978-3-030-58638-6_10
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Volume/pages
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(2020)
, p. 159-175
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ISI
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001285168300010
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Full text (Publisher's DOI)
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Full text (open access)
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