Control Flow Information Analysis in Process Model Matching Techniques

Process model matchers automate the identification of correspondences between process models, i.e., activities that represent similar functionality in different models. This way they support a variety of tasks in business process management, e.g., the management of process model collections, the consolidation of processes, or the re-use of process fragments at design time. To detect correspondences, matchers generally compare activity labels which provide brief natural language descriptions. Additionally, some matchers attach importance to the control flow in the process models and analyze the temporal dependencies between the activities. Yet, the process model matching contests in 2013 and 2015 [Cayoglu et al., 2013, Antunes et al., 2015] revealed that the effectiveness of state-of-the-art matchers is low, i.e., their results only contain a few existing and many irrelevant correspondences. With that in mind, we aim to provide guidance for the development of more effective matchers. In particular, we here examine the question: how can process model matchers benefit from the analysis of control flow information? To answer this question, we review the process model matching literature in order to identify options for using control flow information in the matching process. As we are interested in improving the matcher effectiveness, we further assess the evidence that was given towards the successful contribution of control flow information to the matching process. We limit our focus to control flow information, because the comparison of labels has been extensively studied in natural language processing [Manning and Schütze, 1999], information retrieval [Manning et al., 2008], as well as schema and ontology matching [Euzenat and Shvaiko, 2013, Rahm and Bernstein, 2001]. The remainder of this report is organized as follows. We first outline how we identified process model matching literature in Section 2. Next, we provide a brief overview of the identified literature in Section 3. Then, we present a classification of analysis options that we derived from the literature and discuss the evidence given towards them in Section 4. Finally, Section 5 concludes the report.