Improving next step predictions in processes using contextual event graphs
Leveraging contextual events on structure-aware next activity prediction
Machine LearningArtificial Intelligence
Summary
Predicting what happens next in a process is a common challenge, but many methods miss the bigger picture around the event. The authors introduce a way to include context—like environmental details—by representing process information as graphs. They use these graphs with a type of AI called a Graph Neural Network to better guess the next activity. Tests with real-world data show that adding this context helps the predictions get more accurate.
predictive process monitoringnext activity predictioncontextual informationevent logsInstance GraphsGraph Neural Networkprocess instancesclassification taskprefix graphsenvironmental conditions
Authors
Alessandro Mele, Claudia Diamantini, Domenico Potena
Abstract
Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represents the most extensively investigated. However, only a limited number of existing approaches explicitly encode contextual information, i.e., the environmental conditions in which the process is executed, typically modeled through event log attributes or aggregated measures. In this paper, an approach based on the concept of Instance Graphs is introduced. To incorporate contextual process instances, several encoding strategies are proposed and evaluated by measuring their impact on prediction performance. For each encoding strategy, a set of prefix-Instance Graphs is generated and subsequently provided as input to a Graph Neural Network for the classification task. The proposed approach is evaluated on multiple real-world event logs, and the experimental results demonstrate that incorporating contextual process instances benefits prediction performance.