Associate Professor
Technical University of Denmark
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.
In Online Proceedings of the BPM Demo Track 2014; Haifa, Israel; September, 10 2014; CEUR-WS.org 2014.
Copyright and moral rights for the publications made accessible in the public website are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Latest website update: 18 April 2024.