A toolkit for streaming process data analysis

R.M. Dijkman; S.P.F. Peters; A.H.M. ter Hofstede

doi:10.1109/EDOCW.2016.7584341

A toolkit for streaming process data analysis

R.M. Dijkman, S.P.F. Peters, A.H.M. ter Hofstede

Information Systems IE&IS

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

7 Citations (Scopus)

4 Downloads (Pure)

Abstract

This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.

Original language	English
Title of host publication	2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), 5-9 September 2016, Vienna, Austria
Editors	R. Dijkman, L. Ferreira Pires, S. Rinderle-Ma
Place of Publication	Piscataway
Publisher	IEEE Press
Pages	304-312
Number of pages	9
ISBN (Electronic)	978-1-4673-9933-3
DOIs	https://doi.org/10.1109/EDOCW.2016.7584341
Publication status	Published - 2016
Event	20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016) - University of Vienna, Vienna, Austria Duration: 5 Sept 2016 → 9 Sept 2016

Conference

Conference	20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016)
Abbreviated title	EDOCW 2016
Country/Territory	Austria
City	Vienna
Period	5/09/16 → 9/09/16

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10.1109/EDOCW.2016.7584341

Cite this

@inproceedings{4f56b9dea3ce4383bd6648e2b80ee6a4,

title = "A toolkit for streaming process data analysis",

abstract = "This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.",

author = "R.M. Dijkman and S.P.F. Peters and {ter Hofstede}, A.H.M.",

year = "2016",

doi = "10.1109/EDOCW.2016.7584341",

language = "English",

pages = "304--312",

editor = "R. Dijkman and {Ferreira Pires}, L. and S. Rinderle-Ma",

booktitle = "2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), 5-9 September 2016, Vienna, Austria",

publisher = "IEEE Press",

note = "20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016) , EDOCW 2016 ; Conference date: 05-09-2016 Through 09-09-2016",

}

Dijkman, RM, Peters, SPF & ter Hofstede, AHM 2016, A toolkit for streaming process data analysis. in R Dijkman, L Ferreira Pires & S Rinderle-Ma (eds), 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), 5-9 September 2016, Vienna, Austria. IEEE Press, Piscataway, pp. 304-312, 20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016) , Vienna, Austria, 5/09/16. https://doi.org/10.1109/EDOCW.2016.7584341

A toolkit for streaming process data analysis. / Dijkman, R.M.; Peters, S.P.F.; ter Hofstede, A.H.M.
2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), 5-9 September 2016, Vienna, Austria. ed. / R. Dijkman; L. Ferreira Pires; S. Rinderle-Ma. Piscataway: IEEE Press, 2016. p. 304-312.

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

TY - GEN

T1 - A toolkit for streaming process data analysis

AU - Dijkman, R.M.

AU - Peters, S.P.F.

AU - ter Hofstede, A.H.M.

PY - 2016

Y1 - 2016

N2 - This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.

AB - This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.

U2 - 10.1109/EDOCW.2016.7584341

DO - 10.1109/EDOCW.2016.7584341

M3 - Conference contribution

SP - 304

EP - 312

BT - 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW), 5-9 September 2016, Vienna, Austria

A2 - Dijkman, R.

A2 - Ferreira Pires, L.

A2 - Rinderle-Ma, S.

PB - IEEE Press

CY - Piscataway

T2 - 20th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2016)

Y2 - 5 September 2016 through 9 September 2016

ER -

A toolkit for streaming process data analysis

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