An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
This work addresses a gap for researchers and practitioners in business process management by providing empirical insights to select optimal techniques, though it is incremental as it compares existing methods.
The paper tackled the lack of comparative analysis in predictive business process monitoring by empirically evaluating three deep neural network architectures combined with five encoding techniques on five real-life event logs, finding that certain combinations yield better predictive accuracy for next activity prediction.
Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate predictions, a plethora of these techniques rely on deep neural networks (DNNs) and consider information about the context, in which the process is running. However, an in-depth comparison of such techniques is missing in the PBPM literature, which prevents researchers and practitioners from selecting the best solution for a given event log. To remedy this problem, we empirically evaluate the predictive quality of three promising DNN architectures, combined with five proven encoding techniques and based on five context-enriched real-life event logs. We provide four findings that can support researchers and practitioners in designing novel PBPM techniques for predicting the next activities.