Dual-View Deep Learning Approach for Predictive Business Process Monitoring

Abstract

Predictive business process monitoring (PBPM) is particularly valuable in dynamic business environments, and it can help organisations mitigate risks and optimise resource allocation. An interesting task in PBPM is next activity prediction (NAP), which allows the prediction of future activities that will be executed at a certain time based on ongoing business processes. Existing methods typically only utilise the order information of traces when predicting the next activity, without fully leveraging the attribute information present in the logs. Given the usefulness of these for NAP, combining them can help neural networks gain a deeper understanding of the actual business process. In this study, we propose a dual-view deep learning approach to fully extract and fuse the aforementioned two aspects of information. First, we treated traces as sequential texts and extracted the trace order information based on a long short-term memory based self-attention network. Then, we treated traces as unstructured images and captured the implicit attribute fusion information among events using a 12-layer residual network. Finally, two parts of information were fused for NAP. Experiments on 12 real-life event logs prove that the proposed approach is superior to state-of-the-art approaches, exhibiting good performance in accuracy, macro-precision, macro-recall, macro-F1-score, and macro-Gmean.