Process Mining: Discovering Direct Successors in Process Logs

Laura Maruster; A. J.M.M.(Ton) Weijters; W. M.P.(Wil) van der Aalst; Antal van den Bosch

doi:10.1007/3-540-36182-0_37

Process Mining: Discovering Direct Successors in Process Logs

Laura Maruster
, A. J.M.M.(Ton) Weijters
, W. M.P.(Wil) van der Aalst
, Antal van den Bosch

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

61 Citations (Scopus)

Abstract

Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method.

Original language	English
Title of host publication	Discovery Science - 5th International Conference, DS 2002, Proceedings
Editors	Steffen Lange, Ken Satoh, Carl H. Smith
Publisher	Springer
Pages	364-373
Number of pages	10
ISBN (Electronic)	978-3-540-36182-4
ISBN (Print)	978-3-540-00188-1
DOIs	https://doi.org/10.1007/3-540-36182-0_37
Publication status	Published - 2002
Externally published	Yes
Event	5th International Conference on Discovery Science, DS 2002 - Lubeck, Germany Duration: 24-Nov-2002 → 26-Nov-2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	2534
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Discovery Science, DS 2002
Country/Territory	Germany
City	Lubeck
Period	24/11/2002 → 26/11/2002

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10.1007/3-540-36182-0_37

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Maruster, L., Weijters, A. J. M. M., van der Aalst, W. M. P., & van den Bosch, A. (2002). Process Mining: Discovering Direct Successors in Process Logs. In S. Lange, K. Satoh, & C. H. Smith (Eds.), Discovery Science - 5th International Conference, DS 2002, Proceedings (pp. 364-373). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2534). Springer. https://doi.org/10.1007/3-540-36182-0_37

Maruster, Laura ; Weijters, A. J.M.M.(Ton) ; van der Aalst, W. M.P.(Wil) et al. / Process Mining : Discovering Direct Successors in Process Logs. Discovery Science - 5th International Conference, DS 2002, Proceedings. editor / Steffen Lange ; Ken Satoh ; Carl H. Smith. Springer, 2002. pp. 364-373 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Process Mining: Discovering Direct Successors in Process Logs",

abstract = "Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method.",

author = "Laura Maruster and Weijters, \{A. J.M.M.(Ton)\} and \{van der Aalst\}, \{W. M.P.(Wil)\} and \{van den Bosch\}, Antal",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 5th International Conference on Discovery Science, DS 2002 ; Conference date: 24-11-2002 Through 26-11-2002",

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doi = "10.1007/3-540-36182-0\_37",

language = "English",

isbn = "978-3-540-00188-1",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Maruster, L, Weijters, AJMM, van der Aalst, WMP & van den Bosch, A 2002, Process Mining: Discovering Direct Successors in Process Logs. in S Lange, K Satoh & CH Smith (eds), Discovery Science - 5th International Conference, DS 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2534, Springer, pp. 364-373, 5th International Conference on Discovery Science, DS 2002, Lubeck, Germany, 24/11/2002. https://doi.org/10.1007/3-540-36182-0_37

Process Mining: Discovering Direct Successors in Process Logs. / Maruster, Laura; Weijters, A. J.M.M.(Ton); van der Aalst, W. M.P.(Wil) et al.
Discovery Science - 5th International Conference, DS 2002, Proceedings. ed. / Steffen Lange; Ken Satoh; Carl H. Smith. Springer, 2002. p. 364-373 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2534).

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

TY - GEN

T1 - Process Mining

T2 - 5th International Conference on Discovery Science, DS 2002

AU - Maruster, Laura

AU - Weijters, A. J.M.M.(Ton)

AU - van der Aalst, W. M.P.(Wil)

AU - van den Bosch, Antal

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2002.

PY - 2002

Y1 - 2002

N2 - Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method.

AB - Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method.

UR - https://www.scopus.com/pages/publications/84949789713

U2 - 10.1007/3-540-36182-0_37

DO - 10.1007/3-540-36182-0_37

M3 - Conference contribution

AN - SCOPUS:84949789713

SN - 978-3-540-00188-1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 364

EP - 373

BT - Discovery Science - 5th International Conference, DS 2002, Proceedings

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PB - Springer

Y2 - 24 November 2002 through 26 November 2002

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Maruster L, Weijters AJMM, van der Aalst WMP, van den Bosch A. Process Mining: Discovering Direct Successors in Process Logs. In Lange S, Satoh K, Smith CH, editors, Discovery Science - 5th International Conference, DS 2002, Proceedings. Springer. 2002. p. 364-373. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-36182-0_37

Process Mining: Discovering Direct Successors in Process Logs

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