Automatic root cause identification using most probable alignments

M. Koorneef; A. Solti; H. Leopold; H.A. Reijers

doi:10.1007/978-3-319-74030-0_15

Automatic root cause identification using most probable alignments

M. Koorneef, A. Solti, H. Leopold, H.A. Reijers

Process Science

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

5 Citations (Scopus)

1 Downloads (Pure)

Abstract

In many organizational contexts, it is important that behavior conforms to the intended behavior as specified by process models. Non-conforming behavior can be detected by aligning process actions in the event log to the process model. A probable alignment indicates the most likely root cause for non-conforming behavior. Unfortunately, available techniques do not always return the most probable alignment and, therefore, also not the most probable root cause. Recognizing this limitation, this paper introduces a method for computing the most probable alignment. The core idea of our approach is to use the history of an event log to assign probabilities to the occurrences of activities and the transitions between them. A theoretical evaluation demonstrates that our approach improves upon existing work.

Original language	English
Title of host publication	Business Process Management Workshops - BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers
Editors	E. Teniente, M. Weidlich
Place of Publication	Cham
Publisher	Springer
Pages	204-215
Number of pages	12
ISBN (Electronic)	9783319740300
ISBN (Print)	9783319740294
DOIs	https://doi.org/10.1007/978-3-319-74030-0_15
Publication status	Published - 2018
Event	15th International Conference on Business Process Management (BPM 2017) - Barcelona, Spain Duration: 10 Sept 2017 → 15 Sept 2017 Conference number: 15 https://bpm2017.cs.upc.edu/

Publication series

Name	Lecture Notes in Business Information Processing
Volume	308
ISSN (Print)	1865-1348

Conference

Conference	15th International Conference on Business Process Management (BPM 2017)
Abbreviated title	BPM 2017
Country/Territory	Spain
City	Barcelona
Period	10/09/17 → 15/09/17
Internet address	https://bpm2017.cs.upc.edu/

Keywords

Conformance checking
Most probable alignments
Root cause analysis
Root cause analysis Most probable alignments

Access to Document

10.1007/978-3-319-74030-0_15

Cite this

Koorneef, M., Solti, A., Leopold, H., & Reijers, H. A. (2018). Automatic root cause identification using most probable alignments. In E. Teniente, & M. Weidlich (Eds.), Business Process Management Workshops - BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers (pp. 204-215). (Lecture Notes in Business Information Processing; Vol. 308). Springer. https://doi.org/10.1007/978-3-319-74030-0_15

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abstract = "In many organizational contexts, it is important that behavior conforms to the intended behavior as specified by process models. Non-conforming behavior can be detected by aligning process actions in the event log to the process model. A probable alignment indicates the most likely root cause for non-conforming behavior. Unfortunately, available techniques do not always return the most probable alignment and, therefore, also not the most probable root cause. Recognizing this limitation, this paper introduces a method for computing the most probable alignment. The core idea of our approach is to use the history of an event log to assign probabilities to the occurrences of activities and the transitions between them. A theoretical evaluation demonstrates that our approach improves upon existing work.",

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Koorneef, M, Solti, A, Leopold, H & Reijers, HA 2018, Automatic root cause identification using most probable alignments. in E Teniente & M Weidlich (eds), Business Process Management Workshops - BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. Lecture Notes in Business Information Processing, vol. 308, Springer, Cham, pp. 204-215, 15th International Conference on Business Process Management (BPM 2017), Barcelona, Spain, 10/09/17. https://doi.org/10.1007/978-3-319-74030-0_15

Automatic root cause identification using most probable alignments. / Koorneef, M.; Solti, A.; Leopold, H. et al.
Business Process Management Workshops - BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. ed. / E. Teniente; M. Weidlich. Cham: Springer, 2018. p. 204-215 (Lecture Notes in Business Information Processing; Vol. 308).

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

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AU - Solti, A.

AU - Leopold, H.

AU - Reijers, H.A.

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N2 - In many organizational contexts, it is important that behavior conforms to the intended behavior as specified by process models. Non-conforming behavior can be detected by aligning process actions in the event log to the process model. A probable alignment indicates the most likely root cause for non-conforming behavior. Unfortunately, available techniques do not always return the most probable alignment and, therefore, also not the most probable root cause. Recognizing this limitation, this paper introduces a method for computing the most probable alignment. The core idea of our approach is to use the history of an event log to assign probabilities to the occurrences of activities and the transitions between them. A theoretical evaluation demonstrates that our approach improves upon existing work.

AB - In many organizational contexts, it is important that behavior conforms to the intended behavior as specified by process models. Non-conforming behavior can be detected by aligning process actions in the event log to the process model. A probable alignment indicates the most likely root cause for non-conforming behavior. Unfortunately, available techniques do not always return the most probable alignment and, therefore, also not the most probable root cause. Recognizing this limitation, this paper introduces a method for computing the most probable alignment. The core idea of our approach is to use the history of an event log to assign probabilities to the occurrences of activities and the transitions between them. A theoretical evaluation demonstrates that our approach improves upon existing work.

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Koorneef M, Solti A, Leopold H, Reijers HA. Automatic root cause identification using most probable alignments. In Teniente E, Weidlich M, editors, Business Process Management Workshops - BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. Cham: Springer. 2018. p. 204-215. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-319-74030-0_15

Automatic root cause identification using most probable alignments

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Keywords

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