Translating Process Mining Results into Intelligible Business Information

Abstract

Most business processes are today rooted into an information system recording operational events in log files. Process Mining algorithms exploit this information to discover and qualify differences between observed and modelled process. However, the output of these algorithms are not clearly connected with business properties. Our work faces these limitations by proposing an approach for calibrating Process Mining results based on the Business Rules adopted by an organisation. The general idea relates on applying Process Mining algorithms on subsequent refinements of the event log, filtering process executions based on Business Rules. This way we are able to associate these results with specific characterisations of the process, as entailed by the corresponding Business Rules. This approach is confronted to a real world scenario using data provided by an Italian manufacturing company.