Automatic root cause identification using most probable alignments
| Autor: | Koorneef, M., Solti, A., Leopold, H., Reijers, H.A., Teniente, E., Weidlich, M. |
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| Přispěvatelé: | Knowledge Representation and Reasoning, Business Informatica, Software and Sustainability (S2), Network Institute, Process Science |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Most probable alignments
Process modeling Root cause analysis Most probable alignments Computer science Process (computing) Conformance checking 020207 software engineering 02 engineering and technology Root cause computer.software_genre Identification (information) Core (game theory) 0202 electrical engineering electronic engineering information engineering Root cause analysis 020201 artificial intelligence & image processing Data mining computer Event (probability theory) |
| Zdroj: | Business Process Management Workshops-BPM 2017 International Workshops, Revised Papers, 204-215 STARTPAGE=204;ENDPAGE=215;TITLE=Business Process Management Workshops-BPM 2017 International Workshops, Revised Papers Business Process Management Workshops ISBN: 9783319740294 Business Process Management Workshops Koorneef, M, Solti, A, Leopold, H & Reijers, H A 2018, Automatic root cause identification using most probable alignments . in Business Process Management Workshops-BPM 2017 International Workshops, Revised Papers . Lecture Notes in Business Information Processing, vol. 308, Springer/Verlag, 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 Business Process Management Workshops-BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers, 204-215 STARTPAGE=204;ENDPAGE=215;TITLE=Business Process Management Workshops-BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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