Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction

In this paper, we propose a novel approach to real-time business process monitoring for prediction of abnormal termination. Existing real-time monitoring approaches are difficult to use proactively, owing to unobserved data from gradual process executions. To improve the utility and effectiveness of real-time monitoring, we derived a KNNI (k nearest neighbor imputation)-based LOF (local outlier factor) prediction algorithm. In each monitoring period of an ongoing process instance, the proposed algorithm estimates the distribution of LOF values and the probability of abnormal termination when the ongoing instance is terminated, which estimations are conducted periodically over entire periods. Thereby, we can probabilistically predict outcomes based on the current progress. In experiments conducted with an example scenario, we showed that the proposed predictors can reflect real-time progress and provide opportunities for proactive prevention of abnormal termination by means of an early alarm. With the proposed method, abnormal termination of an ongoing instance can be predicted, before its actual occurrence, enabling process managers to obtain insights into real-time progress and undertake proactive prevention of probable risks, rather than merely reactive correction of risk eventualities.