Orientador: Fernanda Araujo BaiãoAmorim
Data e Hora: 01/09/2022, 09 h
Link Zoom Meeting:
Resumo:
Process Mining techniques have been successfully applied as a data¬driven and domain-aware approach for improving business process performance in several organizations. Among its applications, Deviance Mining aims at uncovering the reasons why a subset of the executions of a business process deviate with respect to its expected or desirable outcomes, thus producing insights towards improving the process operation. However, despite the fact that real-life processes are typically composed by non-atomic events, existing approaches for process mining and deviance mining in particular only deal with atomic events in their experiments. This work proposes a domain-driven method for automatically detecting deviations in processes composed by non-atomic events. The method uses the temporal dimension of non-atomic events to apply deviance mining, generating insights on how the duration and the simultaneous occurrence of events generate deviations and how these deviations affect the results of the processes. The method was successfully applied in the COVID-19 domain, to find which domain¬specific sequences of non-pharmaceutical interventions mostly contributed to slow down the rate of COVID-19 cases in countries around the world.