A Multi-View Deep Learning Approach for Predictive Business Process Monitoring

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this article, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods.

Automated summary

Predictive business process monitoring is an online approach that predicts the unfolding of running traces based on historical event logs. This article proposes a novel predictive process method that combines multi-view learning and deep learning to improve predictive accuracy by considering various information recorded in event logs.