Continuous Event Log Extraction for Process Mining

Abstract: Process mining is the application of data science technologies on transactional business data to identify or monitor processes within an organization. The analyzed data often originates from process-unaware enterprise software, e.g. Enterprise Resource Planning (ERP) systems. The differences in data management between ERP and process mining systems result in a large fraction of ambiguous cases, affected by convergence and divergence. The consequence is a chasm between the process as interpreted by process mining, and the process as executed in the ERP system. In this thesis, a purchasing process of an SAP ERP system is used to demonstrate, how ERP data can be extracted and transformed into a process mining event log that expresses ambiguous cases as accurately as possible. As the content and structure of the event log already define the scope (i.e. which process) and granularity (i.e. activity types), the process mining results depend on the event log quality. The results of this thesis show how the consideration of case attributes, the notion of a case and the granularity of events can be used to manage the event log quality. The proposed solution supports continuous event extraction from the ERP system.