Leveraging contextual events on structure-aware next activity prediction

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.08622 [cs.LG]

(or arXiv:2609.08622v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.08622 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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