A probabilistic approach to event log completeness

Recently, researchers discovered that the major problems of mining event logs is to discover a simple, sound and complete process model. But since the mining techniques can only reproduce the behaviour recorded in the log, the fitness of the reproduced model is a function of the event log completeness. In this paper, a Fuzzy-Genetic Mining model based on Bayesian Scoring Functions (FGM-BSF) which we called probabilistic approach was developed to tackle problems which emanated from the incomplete event logs. The main motivation of using genetic mining for the process discovery is to benefit from the global search performed by the algorithm. The incompleteness in processes deals with uncertainty and is tackled by using the probabilistic nature of the scoring functions in Bayesian network based on a fuzzy logic value prediction. The global search performed by the genetic approach is panacea to dealing with the population that has both good and bad individuals. Hence, the proposed approach helps to enhance a robust fitness function for the genetic algorithm through highlift traces representing only good individuals not detected by mining model without an intelligent system. The implementation of our approach was carried out on java platform with MySQL for event log parsing and preprocessing while the actual discovery was done in ProM. The results showed that the proposed approach achieved 0.98% fitness when compared with existing schemes.