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, Revised Papers |
| Publisher | Springer/Verlag |
| Pages | 204-215 |
| Number of pages | 12 |
| ISBN (Electronic) | 9783319740300 |
| ISBN (Print) | 9783319740294 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 15th International Conference on Business Process Management, BPM 2017 - Barcelona, Spain Duration: 10 Sep 2017 → 15 Sep 2017 |
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 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 10/09/17 → 15/09/17 |
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Keywords
- Conformance checking
- Most probable alignments
- Root cause analysis
Cite this
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Automatic root cause identification using most probable alignments. / Koorneef, Marie; Solti, Andreas; Leopold, Henrik; Reijers, Hajo A.
Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers. Springer/Verlag, 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
TY - GEN
T1 - Automatic root cause identification using most probable alignments
AU - Koorneef, Marie
AU - Solti, Andreas
AU - Leopold, Henrik
AU - Reijers, Hajo A.
PY - 2018
Y1 - 2018
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.
KW - Conformance checking
KW - Most probable alignments
KW - Root cause analysis
UR - http://www.scopus.com/inward/record.url?scp=85041723855&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041723855&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-74030-0_15
DO - 10.1007/978-3-319-74030-0_15
M3 - Conference contribution
SN - 9783319740294
T3 - Lecture Notes in Business Information Processing
SP - 204
EP - 215
BT - Business Process Management Workshops - BPM 2017 International Workshops, Revised Papers
PB - Springer/Verlag
ER -