System inference represents a challenging issue in system theory. To deal with system inference, D2FD (Data to Fuzzy-DEVS) method has been proposed to discover a Fuzzy-DEVS model from event data. This method has been successfully implemented on the process mining framework (ProM). The validation of this method is able to use a morphism-based model approximation at the replicative level. However, there is a need of predictive validation method for emergency. In this paper, we propose a predictive validation method for D2FD method integrated with granger causality. This method is able to generate a new business process model which is causally influenced by its underlying factors. A predictive model is shown in the real case study from Italian University.
In this presentation, we introduce a modeling and simulation methodology to support a holistic analysis of healthcare systems through a stratification of the levels of abstraction into multiple perspectives and their integration in a common simulation framework. In each of the perspectives, models of different components of healthcare system can be developed and coupled together. Concerns from other perspectives are abstracted as parameters (i.e., translation of assumptions and simplifications) in such models. Consequently, the resulting top model within each perspective can be coupled with its experimental frame to run simulations and derive results. Components of the various perspectives are integrated to provide a holistic view of the healthcare problem and system under study. The resulting global model can be coupled with a holistic experimental frame to derive results that couldn't be accurately addressed in any of the perspective taken alone.