Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?

Predictive process monitoring (PPM) enables organizations to predict the behavior of ongoing processes, e.g., the lead time. This is of great interest for knowledge-intensive processes (KIPs), which often cover long time spans. With such insights, resource allocation or customer relationship management could be improved. While already many PPM methods exist, they have not yet been applied to KIPs. Thus, we extend PPM research by using machine learning and natural language processing (NLP) to develop and evaluate a novel text-aware PPM approach tailored towards monitoring KIPs. By developing suitable features and considering various time intervals, our approach encodes and aggregates the event log. Using two real-world event logs, we assess our methodology. We demonstrate that the MAE improves as compared to state-of-the-art PPM methods. It shows that the control flow perspective of KIPs should primarily be neglected, while considering more structured features and unstructured textual information is essential.