Predictive business process monitoring (PBPM) plays an important role in intelligent workflow management by enabling organizations to anticipate future process behavior and support operational decisions. However, many existing approaches represent execution traces primarily as linear prefixes, thereby limiting their capacity to explicitly capture the control-flow semantics of non-sequential processes. To address this limitation, this paper proposes KG-MTPM, a knowledge-graph-enhanced multi-task framework that integrates process-model-level structural knowledge with prefix-level runtime dynamics in a unified predictive architecture. In particular, control-flow relations are organized as a process knowledge graph so that non-linear execution dependencies can be explicitly represented during prediction. Based on the integrated representation, the model jointly predicts the next-activity, next-activity time, and remaining-time of an ongoing case. Experiments on three real-world event log datasets demonstrate that KG-MTPM achieves the best overall performance among the evaluated baselines, with a marked advantage in time-related prediction tasks. Relative to the best-performing baseline, KG-MTPM improves next-activity prediction accuracy from 0.84 to 0.85, while reducing the mean absolute error (MAE) of next-activity time prediction from 0.81 to 0.25 and that of remaining-time prediction from 0.98 to 0.47. Ablation results confirm the contributions of both the structure-aware representation and the multi-task learning scheme. Overall, the findings suggest that explicit modeling of process structure is beneficial for predictive monitoring in business processes with complex execution behavior.
Pan et al. (Sat,) studied this question.