A Structural Anomaly-Trace Detection Approach and its Implications in Process Mining and Reengineering

In order to effectively do the reengineering of process models within managing their lifecycles, it is important to maintain the qualitative levels of control-flow gateway-activity formations of the discovered process models, as highly as possible, in the process mining time. However, almost all the conventional process mining systems are hardly assured the consistent functionality in discovering the higher level of qualitative formations; Setting aside the quality issue, some of them even suffer from discovering inaccurate formations. Therefore, this paper tries to devise an algorithmic approach of detecting a small group of traces in a process log dataset, which may exert strong effects on degrading the qualitative level of control-flow gateway-activity formations of the discovered process model, because these traces negatively affect to form the structural formations during the process mining time. In detail, this paper formulates the new type of exceptional traces, which is named as structural anomaly-trace type, within a process log dataset, and formally defines a series of related conceptual definitions. Next, an algorithmic approach to automatically detect the structural anomaly-traces is described, and its functional correctness is verified through its implemented process mining system operating with an exemplary dataset of the Customer Summary process logs available from the 4TU Center for Research Data. Finally, this paper validates and discusses the feasibility as well the effectiveness of the proposed approach on the process mining time as well as the process reengineering time of the process lifecycle management scheme in process-aware enterprises and organizations.