Time matters: Time-aware LSTMs for predictive business process monitoring
Nguyen A, Chatterjee S, Weinzierl S, Schwinn L, Matzner M, Eskofier B (2020)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2020
Pages Range: 1-12
Conference Proceedings Title: Proceedings of the ICPM 2020 International Workshops
Event location: Padua
DOI: 10.1007/978-3-030-72693-5_9
Abstract
Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on
event log data. Especially, techniques for the next activity and timestamp prediction can help to improve the performance of operational business processes. Recently, many PBPM solutions based on deep learning
were proposed by researchers. Due to the sequential nature of event log
data, a common choice is to apply recurrent neural networks with long
short-term memory (LSTM) cells. We argue, that the elapsed time between events is informative. However, current PBPM techniques mainly
use “vanilla” LSTM cells and hand-crafted time-related control flow features. To better model the time dependencies between events, we propose
a new PBPM technique based on time-aware LSTM (T-LSTM) cells.
T-LSTM cells incorporate the elapsed time between consecutive events
inherently to adjust the cell memory. Furthermore, we introduce costsensitive learning to account for the common class imbalance in event
logs. Our experiments on publicly available benchmark event logs indicate the effectiveness of the introduced techniques.
Authors with CRIS profile
An Nguyen
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Sven Weinzierl
Lehrstuhl für Digital Industrial Service Systems
Leo Schwinn
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Martin Matzner
Lehrstuhl für Digital Industrial Service Systems
Björn Eskofier
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
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How to cite
APA:
Nguyen, A., Chatterjee, S., Weinzierl, S., Schwinn, L., Matzner, M., & Eskofier, B. (2020). Time matters: Time-aware LSTMs for predictive business process monitoring. In Proceedings of the ICPM 2020 International Workshops (pp. 1-12). Padua, IT.
MLA:
Nguyen, An, et al. "Time matters: Time-aware LSTMs for predictive business process monitoring." Proceedings of the International Conference on Process Mining, Padua 2020. 1-12.
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