Process Mining

Abstract

Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process models and are often only used to analyze a specific step in the overall process. Process mining focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process models are used for analysis (e.g., simulation and verification) and enactment by BPM/WFM systems. Previously, process models were typically made by hand without using event data. However, activities executed by people, machines, and software leave trails in so-called event logs . Process mining techniques use such logs to discover, analyze, and improve business processes. Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active involvement of end-users, tool vendors, consultants, analysts, and researchers illustrates the growing significance of process mining as a bridge between data mining and business process modeling. The practical relevance of process mining and the interesting scientific challenges make process mining one of the “hot” topics in Business Process Management (BPM). This article introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto.