Predictive business process monitoring with LSTM neural networks
Tax, Niek, Verenich, Ilya, La Rosa, Marcello, & Dumas, Marlon (2017) Predictive business process monitoring with LSTM neural networks. In 29th International Conference on Advanced Information Systems Engineering (CAiSE2017), 12-16 June 2017, Essen, Germany. (In Press)
Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.
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|Item Type:||Conference Paper|
|Keywords:||Business process management, Process mining, LSTM, Predictive monitoring, Sequence prediction|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)|
|Divisions:||Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Facilities:||Science and Engineering Centre|
|Copyright Owner:||Copyright 2016 [please consult the author]|
|Deposited On:||07 Dec 2016 00:16|
|Last Modified:||12 Mar 2017 23:04|
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