JARVIS: Joining Adversarial Training With Vision Transformers in Next-Activity Prediction

In this paper, we propose a novel predictive process monitoring approach, named
$\mathsf{JARVIS}$
, that is designed to achieve a balance between accuracy and explainability in the task of next-activity prediction. To this aim,
$\mathsf{JARVIS}$
represents different process executions (traces) as patches of an image and uses this patch-based representation within a multi-view learning scheme combined with Vision Transformers (ViTs). Using multi-view learning we guarantee good accuracy by leveraging the variety of information recorded in event logs as different patches of an image. The use of ViTs enables the integration of explainable elements directly into the framework of a predictive process model trained to forecast the next trace activity from the completed events in a running trace by utilizing self-attention modules that give paired attention values between two picture patches. Attention modules disclose explainable information concerning views of the business process and events of the trace that influenced the prediction. In addition, we explore the effect of ViT adversarial training to mitigate overfitting and improve the accuracy and robustness of predictive process monitoring. Experiments with various benchmark event logs prove the accuracy of
$\mathsf{JARVIS}$
compared to several current state-of-the-art methods and draw insights from explanations recovered through the attention modules.