As one of the hot topics in Business Process Management (BPM), process mining aims at constructing models to explain what is actually happening from different perspectives based on the process-related information that automatically extracted from event logs. Because the semantics of the data that recorded in event logs are not usually explicit, current mining approaches are somewhat limited. A number of studies have been carried out in the combination use of formalized semantic models and process mining technologies to obtain the semantic mining capability. However, among these researches, there is lack of a guideline that can clearly illustrate different stages during the semantic process mining. The objective of this study is to present a general framework, which unambiguously expresses the main stages of the semantic process mining. Based on this framework, an example about carbon footprint analysis is used to show the possibility of obtaining advantages from semantic process mining.
Extracting Semantic Process Information from the Natural Language in Event Logs
