Data streams in ProM 6 : a single-node architecture

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

Data streams in ProM 6 : a single-node architecture

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

Process Analytics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

170 Downloads (Pure)

Abstract

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 language	English
Title of host publication	BPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014)
Editors	L. Limonad, B. Weber
Publisher	CEUR-WS.org
Pages	81-85
Publication status	Published - 2014
Event	12th International Conference on Business Process Management, BPM 2014 - Eindhoven, Netherlands Duration: 7 Sept 2014 → 11 Sept 2014 Conference number: 12 http://bpm2014.haifa.ac.il/evover.net_9090/bpm2014.html https://link.springer.com/book/10.1007/978-3-319-15895-2

Publication series

Name	CEUR Workshop Proceedings
Volume	1295
ISSN (Print)	1613-0073

Conference

Conference	12th International Conference on Business Process Management, BPM 2014
Abbreviated title	BPM 2014
Country/Territory	Netherlands
City	Eindhoven
Period	7/09/14 → 11/09/14
Other	Conference 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	http://bpm2014.haifa.ac.il/evover.net_9090/bpm2014.html https://link.springer.com/book/10.1007/978-3-319-15895-2

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@inproceedings{b4c8202367dc4c38821bdf9ba8709603,

title = "Data streams in ProM 6 : a single-node architecture",

abstract = "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.",

author = "{Zelst, van}, S.J. and A. Burattin and {Dongen, van}, B.F. and H.M.W. Verbeek",

year = "2014",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

pages = "81--85",

editor = "L. Limonad and B. Weber",

booktitle = "BPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014)",

note = "12th International Conference on Business Process Management, BPM 2014, BPM 2014 ; Conference date: 07-09-2014 Through 11-09-2014",

url = "http://bpm2014.haifa.ac.il/evover.net_9090/bpm2014.html, https://link.springer.com/book/10.1007/978-3-319-15895-2",

}

Zelst, van, SJ, Burattin, A, Dongen, van, BF & Verbeek, HMW 2014, Data streams in ProM 6 : a single-node architecture. in L Limonad & B Weber (eds), BPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014). CEUR Workshop Proceedings, vol. 1295, CEUR-WS.org, pp. 81-85, 12th International Conference on Business Process Management, BPM 2014, Eindhoven, Netherlands, 7/09/14.

Data streams in ProM 6 : a single-node architecture. / Zelst, van, S.J.; Burattin, A.; Dongen, van, B.F. et al.
BPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014). ed. / L. Limonad; B. Weber. CEUR-WS.org, 2014. p. 81-85 (CEUR Workshop Proceedings; Vol. 1295).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Data streams in ProM 6 : a single-node architecture

AU - Zelst, van, S.J.

AU - Burattin, A.

AU - Dongen, van, B.F.

AU - Verbeek, H.M.W.

N1 - Conference code: 12

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

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EP - 85

BT - BPM Demo Sessions 2014 (co-located with BPM 2014, Eindhoven, The Netherlands, September 20, 2014)

A2 - Limonad, L.

A2 - Weber, B.

PB - CEUR-WS.org

T2 - 12th International Conference on Business Process Management, BPM 2014

Y2 - 7 September 2014 through 11 September 2014

ER -

Data streams in ProM 6 : a single-node architecture

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