Abstract. Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution.
In REF , the authors propose an approach for predicting of time-related process risks by identifying (using statistical principles) indicators observable in event logs that highlight the possibility of transgressing deadlines.
Predicting deadline transgressions using event logs
In Proc. of BPM Workshop 2012, volume 132 of LNBIP
Computer Science Engineering