KNNI-SVM: A hybrid algorithm integrating imputation and support vector machine for real-time business process monitoring
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, B. | - |
dc.contributor.author | Lee, J. | - |
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Kang, S.-H. | - |
dc.date.available | 2018-05-10T13:40:26Z | - |
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/14486 | - |
dc.description.abstract | Inductive data mining based approaches to process monitoring aim at investigating historical process logs and classifying the result of executed process. However, they show some limitations when applied to real-time monitoring such as late warnings or no real-time feedback capabilities. In order to alleviate such limitations, we propose a novel approach to real-time business process monitoring using a hybrid algorithm integrating support vector machine with k nearest neighbor imputation. By the proposed algorithm, an ongoing process instance is monitored through generated attributes and the final result of the instance is predicted based on them, which is iterated periodically as the ongoing instance progresses. Therefore, we can predict probable outcomes probabilistically based on the current progress. © 2011 ISSN 1881-803X. | - |
dc.relation.isPartOf | ICIC Express Letters | - |
dc.subject | Business Process | - |
dc.subject | Historical process | - |
dc.subject | Hybrid algorithms | - |
dc.subject | Imputation | - |
dc.subject | K-nearest neighbors | - |
dc.subject | Process instances | - |
dc.subject | Real-time business | - |
dc.subject | Real-time feedback | - |
dc.subject | Real-time monitoring | - |
dc.subject | Support vector | - |
dc.subject | Process control | - |
dc.subject | Process monitoring | - |
dc.subject | Support vector machines | - |
dc.subject | Algorithms | - |
dc.title | KNNI-SVM: A hybrid algorithm integrating imputation and support vector machine for real-time business process monitoring | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | ICIC Express Letters, v.5, no.8 B, pp.2863 - 2868 | - |
dc.description.journalClass | 1 | - |
dc.identifier.scopusid | 2-s2.0-79960888307 | - |
dc.citation.endPage | 2868 | - |
dc.citation.number | 8 B | - |
dc.citation.startPage | 2863 | - |
dc.citation.title | ICIC Express Letters | - |
dc.citation.volume | 5 | - |
dc.contributor.affiliatedAuthor | Kim, D. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Business process | - |
dc.subject.keywordAuthor | Imputation | - |
dc.subject.keywordAuthor | Real-time monitoring | - |
dc.subject.keywordAuthor | Support vector machine | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL UNIVERSITY, ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.