Experimental Analyses of Temporal Activity-Sequencing Anomalies in Process Mining

Abstract

In the research field of the automated process discovery and analysis, the purity of event log datasets ought to be a matter of vital importance to the success of discovering sound and exact process models. Moreover, there exist various types of anomalies that result in the discovery of inaccurate process models from the process enactment event log datasets. A peculiar one out of these anomalies, which is the core challenging issue of this paper, is the temporal activity-sequencing anomaly that critically affects the overall quality of the automated process discovery. This paper explores such event-log anomalies and noises produced by the special type of anomalies inevitably formed in the event-log preprocessing phase of the automated process discovery. More precisely, it implements an algorithmic approach that is able to detect and filter out those anomalies and noises in performing the automated process discovery. The author also carries out a series of experimental analyses by applying the implemented approach to the five datasets of process event logs available in the 4TU Center for Research Data.