From a prescriptive viewpoint, eventually, DAISYnt may pave the way to synthetic data adoption in highly regulated domains, ranging from Finance to Healthcare, through Insurance and Education.
As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative.
Recently, also machine and deep learning techniques have been applied to the CRM task, showing an important increase in prediction quality and performances.
In this paper, we highlight a trade-off between explanation's stability and adherence, namely how much it resembles the ML model.
We test LIME on the Machine Learning algorithm and check its stability.
The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model.
In the policy making process a number of disparate and diverse issues such as economic development, environmental aspects, as well as the social acceptance of the policy, need to be considered.