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Context-Aware Prediction on Business Process Executions

Discovering predictive performance models is an emerging topic in Process Mining. However, making accurate estimates is not easy especially when considering fine-grain metrics (such as processing times) on complex and flexible processes, where performances may change over time depending on context factors. We try to face such a situation by a general predictive-clustering approach, where different context-related execution scenarios are find and equipped with distinct performanceprediction models. A two-stage forecast can be then made for a new process case by using the model of the cluster it is estimated to belong to. Tests on real-life logs confirmed the validity of the approach.