dc.citation.conferencePlace |
CC |
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dc.citation.conferencePlace |
Hangzhou |
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dc.citation.endPage |
373 |
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dc.citation.startPage |
367 |
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dc.citation.title |
16th International Conference on Service-Oriented Computing, ICSOC 2018 |
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dc.contributor.author |
Comuzzi, Marco |
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dc.contributor.author |
Marquez-Chamorro, Alfonso E. |
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dc.contributor.author |
Resinas, Manuel |
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dc.date.accessioned |
2024-02-01T01:06:31Z |
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dc.date.available |
2024-02-01T01:06:31Z |
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dc.date.created |
2019-05-16 |
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dc.date.issued |
2018-11-12 |
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dc.description.abstract |
The evaluation of business process predictive monitoring models usually focuses on accuracy of predictions. While accuracy aggregates performance across a set of process cases, in many practical scenarios decision makers are interested in the reliability of an individual prediction, that is, an indication of how likely is a given prediction to be eventually correct. This paper proposes a first definition of business process prediction reliability and shows, through the experimental evaluation, that metrics that include features defining the variability of a process case often give a better prediction reliability indication than metrics that include the probability estimation computed by the machine learning model used to make predictions alone. |
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dc.identifier.bibliographicCitation |
16th International Conference on Service-Oriented Computing, ICSOC 2018, pp.367 - 373 |
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dc.identifier.doi |
10.1007/978-3-030-17642-6_30 |
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dc.identifier.issn |
0302-9743 |
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dc.identifier.scopusid |
2-s2.0-85064855971 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/80459 |
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dc.identifier.url |
https://link.springer.com/chapter/10.1007%2F978-3-030-17642-6_30 |
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dc.language |
영어 |
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dc.publisher |
ICSOC |
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dc.title |
Does your accurate process predictive monitoring model give reliable predictions? |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2018-11-12 |
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