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Extended KNN Imputation Based LOF Prediction Algorithm for Real-time Business Process Monitoring Method  

Kang, Bok-Young (서울대학교 산업공학과)
Kim, Dong-Soo (숭실대학교 산업정보시스템공학과)
Kang, Suk-Ho (서울대학교 산업공학과)
Publication Information
The Journal of Society for e-Business Studies / v.15, no.4, 2010 , pp. 303-317 More about this Journal
Abstract
In this paper, we propose a novel approach to fault prediction for real-time business process monitoring method using extended KNN imputation based LOF prediction. Existing rule-based approaches to process monitoring has some limitations like late alarm for fault occurrence or no indicators about real-time progress, since there exist unobserved attributes according to the monitoring phase during process executions. To improve these limitations, we propose an algorithm for LOF prediction by adopting the imputation method to assume unobserved attributes. LOF of ongoing instance is calculated by assuming next probable progresses after the monitoring phase, which is conducted during entire monitoring phases so that we can predict the abnormal termination of the ongoing instance. By visualizing the real-time progress in terms of the probability on abnormal termination, we can provide more proactive operations to opportunities or risks during the real-time monitoring.
Keywords
LOF(Local Outlier Factor); Imputation; Real-time; Process Monitoring; Outlier Detection and Prediction;
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