Deep Analysis of Process Model Matching Techniques

Abstract

Process Model Matching (PMM) aims to automatically identify corresponding activities from two process models that exhibit similar behaviors. Recognizing the diverse applications of process model matching, several techniques have been proposed in the literature. Typically, the effectiveness of these matching techniques has been evaluated using three widely used performance measures, Precision, Recall, and F1 score. In this study, we have established that the values of these three measures for each dataset do not provide deeper insights into the capabilities of the matching techniques. To that end, we have made three significant contributions. Firstly, we have enhanced four benchmark datasets by classifying their corresponding activities into three sub-types. The enhanced datasets can be used for surface-level evaluation, as well as a deeper evaluation of matching techniques. Secondly, we have conducted a systematic search of the literature to identify an extensive set of 27 matching techniques and subsequently proposed a taxonomy for these matching techniques. Finally, we have performed 432 experiments to evaluate the effectiveness of all the matching techniques, and key observations about the effectiveness of the techniques are presented.