Process Mining and Automatic Process Discovery

Abstract

Business Processes Modeling is essential for the management and execution of processes. However, when the execution of the processes differs from the pre-established models is necessary to review the traces and records of events to know these differences. One goal of process mining is to discover the real processes through the extraction of knowledge from the records of events available in the information systems. This paper describes a systematic literature review to identify the algorithms developed for automatic discovery of business processes. 20 articles that included algorithm proposals were identified and the results show that the algorithms provide similar models to the records of events when these events are clean without noise. In addition, it is observed that the most used technique to model the flows is Petri net.