Ontological approach to enhance results of business process mining and analysis

Purpose The purpose of this paper is to propose a solution to the problem of a lack of machine processable semantics in business process management. Design/methodology/approach The paper introduces a methodology that combines domain and company‐specific ontologies and databases to obtain multiple levels of abstraction for process mining and analysis. The authors valuated this approach with a real case study from the apparel domain, using a prototype system and techniques developed in the Process Mining Framework (ProM). The results of this approach are compared with similar research. Findings Semantically enriching process execution data can successfully raise analysis from the syntactic to the semantic level, and enable multiple perspectives of analysis on business processes. Combining this approach with complementary research in semantic business process management (SBPM) can provide results comparable to multidimensional analysis in data warehouse and on line analytical processing (OLAP) technologies. Originality/value The approach and prototype described in this paper improve the richness of semantics available for open‐source process mining and analysis tools like ProM, and the richness and detail of the resulting analysis.