A clustering based incremental faulty-rate estimation algorithm for business process monitoring
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, B. | - |
dc.contributor.author | Lee, J. | - |
dc.contributor.author | Kim, S. | - |
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Kang, S.-H. | - |
dc.date.available | 2018-05-10T13:34:08Z | - |
dc.date.created | 2018-04-17 | - |
dc.date.issued | 2011 | - |
dc.identifier.issn | 1881-803X | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/14453 | - |
dc.description.abstract | This paper proposes a novel approach to real-time business process monitoring using a newly proposed clustering based incremental faulty-rate estimation algorithm. In our approach, faulty-rate is defined as a probability that an ongoing process might be ended in fault, which can be estimated based on observed attributes and possible outcomes at each monitoring phase. In the proposed estimation algorithm, unobserved attributes are substituted by historical distributions so that the faulty-rate can be derived in a distribution function. Finally, how the faulty-rate estimation can be applied to the real-time process monitoring is illustrated with an example scenario. ICIC International © 2011 ISSN 1881-803X. | - |
dc.relation.isPartOf | ICIC Express Letters | - |
dc.subject | Business process monitoring | - |
dc.subject | Clustering | - |
dc.subject | Estimation algorithm | - |
dc.subject | Gaussian mixture | - |
dc.subject | Rate estimation | - |
dc.subject | Rate estimation algorithms | - |
dc.subject | Real-time business | - |
dc.subject | Real-time process monitoring | - |
dc.subject | Distribution functions | - |
dc.subject | Estimation | - |
dc.subject | Fault detection | - |
dc.subject | Process control | - |
dc.subject | Process monitoring | - |
dc.subject | Clustering algorithms | - |
dc.title | A clustering based incremental faulty-rate estimation algorithm for business process monitoring | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | ICIC Express Letters, v.5, no.4 B, pp.1261 - 1266 | - |
dc.description.journalClass | 1 | - |
dc.identifier.scopusid | 2-s2.0-79952378701 | - |
dc.citation.endPage | 1266 | - |
dc.citation.number | 4 B | - |
dc.citation.startPage | 1261 | - |
dc.citation.title | ICIC Express Letters | - |
dc.citation.volume | 5 | - |
dc.contributor.affiliatedAuthor | Kim, D. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Clustering | - |
dc.subject.keywordAuthor | Fault detection | - |
dc.subject.keywordAuthor | Gaussian mixture | - |
dc.subject.keywordAuthor | Real-time process monitoring | - |
dc.description.journalRegisteredClass | scopus | - |
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