Predictive Process Monitoring (PPM) aims to predict the future states of ongoing process instances. A primary objective is to accurately predict process outcomes while ensuring decision transparency, which is critical for enhancing process efficiency and reducing operational risk. Existing interpretable approaches to process monitoring often struggle with balancing transparency and reliability. Specifically, approaches that prioritize transparency often fall short in predictive accuracy and generalization, while those that achieve higher prediction performance often provide less reliable explanations. To address these limitations, we propose a novel Transparent Process Outcome Prediction framework (TPOP) using a graph neural network with stochastic attention. We begin by applying a SHAP-based feature selection technique to identify and extract the most relevant attributes from the log, thereby improving the quality of graph-based process representations. Next, we introduce a graph stochastic attention mechanism, which helps the model in concentrate on key paths and activities during training, leading to transparent and trustworthy predictions. Experimental evaluations on ten real-life event logs demonstrate that our approach outperforms state-of-the-art approaches in both predictive performance and interpretability. Furthermore, by visualizing how specific activities influence process outcomes across various cases, we confirm the reliability of the explanations generated by our approach.

ABSTRACT Unexpected behavior in business process executions can be identified to give industrial internet systems security assurance for reliable operation. Current research primarily employs consistency analysis or outlier detection of data points to recognize aberrant behavior, neglecting the relationship between behavior and data properties. This work presents a multiperspective association rule‐based approach for detecting anomalous behavior in industrial processes. Initially, a log transaction table with behavior relationships is constructed by mining behavior associations and related properties from the data Petri net. Subsequently, through the application of context awareness, behavior‐attribute‐time associations of frequently occurring itemsets are generated, and pruning procedures are used to mine multiperspective behavior rules under attribute associations. This approach facilitates the identification of anomalous behavior by comparing the support between logs and rules. Ultimately, the proposed method is implemented using the pm4py open‐source framework, and evaluations are performed on both simulated and real event logs using multiple metrics. Experimental comparison results demonstrate that the proposed anomaly behavior detection method achieves higher performance.