An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

Abstract

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.

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Cited by in corpus (6)

• Prescriptive Business Process Monitoring for Recommending Next Best Actions
• ProcessTransformer: Predictive Business Process Monitoring with Transformer Network
• XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
• Can recurrent neural networks learn process model structure?
• Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
• Can deep neural networks learn process model structure? An assessment framework and analysis