Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators (PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.
|Titel||Advanced Information Systems Engineering (25th International Conference, CAiSE 2013, Valencia, Spain, June 17-21, 2013. Proceedings)|
|Redacteuren||C. Salinesi, M.C. Norrie, O. Pastor|
|Plaats van productie||Berlin|
|ISBN van geprinte versie||978-3-642-38708-1|
|Status||Gepubliceerd - 2013|
|Naam||Lecture Notes in Computer Science|