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Experimental Analyses of Temporal Activity-Sequencing Anomalies In Process Mining
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 cause to discover 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 seriously affects the overall quality of the automated process discovery. In this paper, we explore 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, we implement an algorithmic approach that is able to detect and filter out those anomalies and noises in performing the automated process discovery. We also carry out a series of experimental analyses by applying the implemented approach to the five datasets of process event logs available in the 4TU Centre for Research Data.