Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis.

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis.