Uncovering the Hidden Significance of Activities Location in Predictive Process Monitoring

Predictive process monitoring methods predict ongoing case outcomes by analyzing historical process data. Recent studies highlighted the increasing need to enhance the interpretability of these prediction models. This is often achieved by exploiting post-hoc explainable methodologies to assess the importance of different process features on the predicted outcome. However, the significance of the location of process activities on prediction models remains unexplored. In several real-life contexts, there might be potential meaningful relations between the location of the activities and process outcome. This information facilitates insights into process management optimization and decision-making. This paper
introduces a novel post-hoc explainable artificial intelligence technique inspired by permutation feature importance to assess the impact of activity locations in predictive models. The experimental results on real-life event logs validate the feasibility of the proposed method, showcasing the influence of the location of (group of) activities on outcome predictions.