A Real-Time Method for Detecting Temporary Process Variants in Event Log Data

S. Chouhan; A. Wilbik; R. Dijkman

doi:10.1007/978-3-030-85469-0_14

A Real-Time Method for Detecting Temporary Process Variants in Event Log Data

S. Chouhan^*, A. Wilbik, R. Dijkman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

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Abstract

During the execution of a business process, organizations or individual employees may introduce mistakes, as well as temporary or permanent changes to the process. Such mistakes and changes in the process can introduce anomalies and deviations in the event logs, which in turn introduce temporary and periodic process variants. Early identification of such deviations from the most common types of cases can help an organization to act on them. Keeping this problem in focus, we developed a method that can discover temporary and periodic changes to processes in event log data in real-time. The method classifies cases into common, periodic, temporary, and anomalous cases. The proposed method is evaluated using synthetic and real-world data with promising results.

Original language	English
Title of host publication	BUSINESS PROCESS MANAGEMENT (BPM 2021)
Editors	A. Polyvyanyy, M.T. Wynn, A. van Looy, M. Reichert
Publisher	Springer International Publishing
Pages	197-214
Number of pages	18
Volume	12875
ISBN (Electronic)	978-3-030-85469-0
ISBN (Print)	9783030854683
DOIs	https://doi.org/10.1007/978-3-030-85469-0_14
Publication status	Published - 2021
Event	19th International Conference on Business Process Management - Rome, Italy Duration: 6 Sept 2021 → 10 Sept 2021 Conference number: 19

Publication series

Series	Lecture Notes in Computer Science
Volume	12875
ISSN	0302-9743

Conference

Conference	19th International Conference on Business Process Management
Abbreviated title	BPM 2021
Country/Territory	Italy
City	Rome
Period	6/09/21 → 10/09/21

Keywords

Process discovery
Fuzzy clustering
Process variant
OUTLIER DETECTION
BUSINESS

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10.1007/978-3-030-85469-0_14

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title = "A Real-Time Method for Detecting Temporary Process Variants in Event Log Data",

abstract = "During the execution of a business process, organizations or individual employees may introduce mistakes, as well as temporary or permanent changes to the process. Such mistakes and changes in the process can introduce anomalies and deviations in the event logs, which in turn introduce temporary and periodic process variants. Early identification of such deviations from the most common types of cases can help an organization to act on them. Keeping this problem in focus, we developed a method that can discover temporary and periodic changes to processes in event log data in real-time. The method classifies cases into common, periodic, temporary, and anomalous cases. The proposed method is evaluated using synthetic and real-world data with promising results.",

keywords = "Process discovery, Fuzzy clustering, Process variant, OUTLIER DETECTION, BUSINESS",

author = "S. Chouhan and A. Wilbik and R. Dijkman",

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publisher = "Springer International Publishing",

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Chouhan, S, Wilbik, A & Dijkman, R 2021, A Real-Time Method for Detecting Temporary Process Variants in Event Log Data. in A Polyvyanyy, MT Wynn, A van Looy & M Reichert (eds), BUSINESS PROCESS MANAGEMENT (BPM 2021). vol. 12875, Springer International Publishing, Lecture Notes in Computer Science, vol. 12875, pp. 197-214, 19th International Conference on Business Process Management, Rome, Italy, 6/09/21. https://doi.org/10.1007/978-3-030-85469-0_14

A Real-Time Method for Detecting Temporary Process Variants in Event Log Data. / Chouhan, S.; Wilbik, A.; Dijkman, R.
BUSINESS PROCESS MANAGEMENT (BPM 2021). ed. / A. Polyvyanyy; M.T. Wynn; A. van Looy; M. Reichert. Vol. 12875 Springer International Publishing, 2021. p. 197-214 (Lecture Notes in Computer Science, Vol. 12875).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

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AU - Dijkman, R.

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AB - During the execution of a business process, organizations or individual employees may introduce mistakes, as well as temporary or permanent changes to the process. Such mistakes and changes in the process can introduce anomalies and deviations in the event logs, which in turn introduce temporary and periodic process variants. Early identification of such deviations from the most common types of cases can help an organization to act on them. Keeping this problem in focus, we developed a method that can discover temporary and periodic changes to processes in event log data in real-time. The method classifies cases into common, periodic, temporary, and anomalous cases. The proposed method is evaluated using synthetic and real-world data with promising results.

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KW - Fuzzy clustering

KW - Process variant

KW - OUTLIER DETECTION

KW - BUSINESS

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DO - 10.1007/978-3-030-85469-0_14

M3 - Chapter

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VL - 12875

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BT - BUSINESS PROCESS MANAGEMENT (BPM 2021)

A2 - Polyvyanyy, A.

A2 - Wynn, M.T.

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PB - Springer International Publishing

T2 - 19th International Conference on Business Process Management

Y2 - 6 September 2021 through 10 September 2021

ER -

A Real-Time Method for Detecting Temporary Process Variants in Event Log Data

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