Data streams in ProM 6 : a single-node architecture

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

6 Citaten (Scopus)
138 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.
Originele taal-2Engels
TitelBPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014)
RedacteurenL. Limonad, B. Weber
StatusGepubliceerd - 2014
Evenement12th International Conference on Business Process Management (BPM 2014) - Eindhoven, Nederland
Duur: 7 sep 201411 sep 2014
Congresnummer: 12

Publicatie series

NaamCEUR Workshop Proceedings
ISSN van geprinte versie1613-0073


Congres12th International Conference on Business Process Management (BPM 2014)
Verkorte titelBPM 2014
Ander12th International Conference on Business Process Management
Internet adres


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