Process mining is an active field of research that primarily builds upon data mining and process model-driven analysis. Within the field, static data is typically used. The usage of dynamic and/or volatile data (i.e. real-time streaming data) is very limited. Current process mining techniques are in general not able to cope with challenges posed by real-time data. Hence new approaches that enable us to apply process mining on such data are an interesting new field of study. The ProM-framework that supports a variety of researchers and domain experts in the field has therefore been extended with support for data-streams. This paper gives an overview of the newly created extension that lays a foundation for integrating streaming environments with ProM. Additionally a case study is presented in which a real-life online data stream has been incorporated in a basic ProM-based analysis.
Published in Online Proceedings of the BPM Demo Track 2014; Haifa, Israel; September, 10 2014; CEUR-WS.org 2014.