Predicting critical behaviors in business process executions: when evidence counts

Organizations need to monitor the execution of their processes to ensure they comply with a set of constraints derived, e.g., by internal managerial choices or by external legal requirements. However, preventive systems that enforce users to adhere to the prescribed behavior are often too rigid for real-world processes, where users might need to deviate to react to unpredictable circumstances. An effective strategy for reducing the risks associated with those deviations is to predict whether undesired behaviors will occur in running process executions, thus allowing a process analyst to promptly respond to such violations. In this work, we present a predictive process monitoring technique based on Subjective Logic. Compared to previous work on predictive monitoring, our approach allows to easily customize both the reliability and sensitivity of the predictive system. We evaluate our approach on synthetic data, also comparing it with previous work.