Business Process Failure Prediction: a case study - Mello, Pedro O. T.; Revoredo, Kate; Santoro, Flávia (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2019)
Business process monitoring aims at maintaining the reliability of process executions. However, the dynamic nature of business processes hinders a proactive scenario in which risk mitigation actions can occur before the facts that put the process at risk. Thus, some premises are necessary such as the identification of situations and patterns in historical data of the processes execution in order to characterize what determined the failures. In this paper, we address the problem of how to identify and detect patterns of behaviors that can lead the processes to a failure situation. As a solution, a combination of well-established techniques from Data and Process Mining fields are applied in a case study of an incident management process. The results obtained open possibilities to a proactive scenario.