Data streams in ProM 6 : a single-node architecture

S.J. Zelst, van, A. Burattin, B.F. Dongen, van, H.M.W. Verbeek

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
154 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationBPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014)
EditorsL. Limonad, B. Weber
Publication statusPublished - 2014
Event12th International Conference on Business Process Management, BPM 2014 - Eindhoven, Netherlands
Duration: 7 Sept 201411 Sept 2014
Conference number: 12

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


Conference12th International Conference on Business Process Management, BPM 2014
Abbreviated titleBPM 2014
OtherConference was originally planned in Haifa, but due to the unstable situation in southern Israel, it was relocated to Eindhoven. BPM Demo Sessions 2014, BPMD 2014, Co-located with the 12th International Conference on Business Process Management, BPM 2014
Internet address


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