Adaptive process model matching: improving the effectiveness of label-based matching through automated configuration and expert feedback
thesisposted on 2022-03-28, 00:49 authored by Christopher Klinkmüller
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, all data is used for the matcher development and the validity of the design decisions is not studied. A result of these shortcomings is that existing matchers yield a varying and typically low effectiveness when applied to different datasets. With that 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 more detail, the thesis develops matching techniques 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. In comparison to the state of the art it achieves a high effectiveness. However, its effectiveness depends on the degree to which the underlying knowledge sources reflect the domain characteristics of the models. Moreover, it needs to be configured for each model collection which can require a huge manual effort. Second, the Order Preserving Bag-of-Words Technique analyzes control flow dependencies between activities in order to automatically configure the Bag-of-Words Technique and to maximize its effectiveness. Thus, it relieves experts from manually configuring the matchers. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections and to further improve the effectiveness. Here, the Adaptive Bag-of-Words Technique is introduced. It analyzes expert feedback in order to continuously adjust the matching process and yields a strongly improved effectiveness. Consequently, it outperforms state-of-the-art matchers as well as the other matchers from this thesis.