Provided by: University of Udine
Topic: Big Data
Contemporary information systems struggle with the requirement to provide flexibility and process support while still enforcing some degree of control. Workflow management systems are typically considered as too restrictive while groupware applications (e.g., e-mail) tend to offer hardly any process support or control at all. Therefore, the authors consider adaptive Process Management Systems that allow for run-time changes at both process type and process instance level (e.g., a system like ADEPT). Assuming that these process changes are recorded explicitly, they discuss how this information can be used as input for process mining. So far, process mining has only be applied to operational processes, i.e., knowledge is extracted from event logs (process discovery), or event logs are compared with some a-priori model (conformance checking).