BPMGLAM : A Predictive Business Process Monitoring Framework Based on Graph Contrastive Learning and Attention Mechanisms

Predictive process monitoring is a log analysis technique that combines data and processes. It predicts the future behavior of ongoing cases based on historical process execution records, thereby supporting rational resource allocation and intelligent process decision making. Existing methods based on deep learning techniques mainly rely on single‐case trends to predict the next activity or remaining time, neglecting the impact of global structural context on the prediction of currently ongoing cases. To address this limitation, this paper proposes a Predictive Business Process Monitoring framework based on Graph contrastive Learning and Attention Mechanism (BPMGLAM). The framework focuses on both direct and indirect relationships between activities to construct a global graph structure. Through a contrastive learning strategy between global graph‐related subgraphs and local graphs, it enhances the structural representation capability of ongoing cases and integrates multi‐level event attribute information to generate reliable predictions of future activities. Experiments conducted on multiple real‐world event logs demonstrate that the proposed method achieves superior prediction performance.