A Short Survey on Process Model Similarity <s> Future Research on Process Model Similarity <s> It is common for large organizations to maintain repositories of business process models in order to document and to continuously improve their operations. Given such a repository, this paper deals with the problem of retrieving those models in the repository that most closely resemble a given process model or fragment thereof. Up to now, there is a notable research gap on comparing different approaches to this problem and on evaluating them in the same setting. Therefore, this paper presents three similarity metrics that can be used to answer queries on process repositories: (i) node matching similarity that compares the labels and attributes attached to process model elements; (ii) structural similarity that compares element labels as well as the topology of process models; and (iii) behavioral similarity that compares element labels as well as causal relations captured in the process model. These metrics are experimentally evaluated in terms of precision and recall. The results show that all three metrics yield comparable results, with structural similarity slightly outperforming the other two metrics. Also, all three metrics outperform text-based search engines when it comes to searching through a repository for similar business process models. <s> BIB001 </s> A Short Survey on Process Model Similarity <s> Future Research on Process Model Similarity <s> Business process models are extensively used in companies to document and improve business operations. In essence, there are two major challenges. The increasing number of staff with little modeling expertise involved in model design requires new concepts for quality assurance. Moreover, the huge number of process models typically maintained in a model repository impedes extraction of general process knowledge, which can be used for assistance. This article investigates action patterns as a means to address these challenges. Action patterns capture chunks of actions often appearing together in business processes. We formalize the action pattern concept, including several types of behavioral connection, different abstraction levels, and varying action sensitivity to business objects. Our concepts are evaluated based on a prototypical implementation, which we use to extract various types of action patterns from two industrial process model collections. The results demonstrate that action patterns occurring in different application domains can be discovered. <s> BIB002 </s> A Short Survey on Process Model Similarity <s> Future Research on Process Model Similarity <s> Similarity measures for business process models have been suggested for different purposes such as measuring compliance between reference and actual models, searching for related models in a repository, or locating services that adhere to a specification given by a process model. The aim of our article is to provide a comprehensive survey on techniques to define and calculate such similarity measures. As the measures differ in many aspects, it is an interesting question how different measures rank ''similarity'' within the same set of models. We investigated, how different kinds of changes in a model influence the values of different similarity measures that have been published in academic literature. Furthermore, we identified eight properties that a similarity measure should have from a theoretical point of view and analysed how these properties are fulfilled by the different measures. Our results show that there are remarkable differences among existing measures. We give some recommendations which type of measure is useful for which kind of application. <s> BIB003 </s> A Short Survey on Process Model Similarity <s> Future Research on Process Model Similarity <s> Although increasingly IT-supported, effective techniques for process model retrieval and identification of process model differences or changes - needed for a variety of management and conformance purposes - are still challenging problems in business process management. Performing automated process comparison and finding relevant reference processes are not routine procedures for today's operational process repositories. Efficient combinations of similarity measures for various process model characteristics can still improve the performance of process comparison and retrieval. The approach at hand introduces the concept of related cluster pairs, parameterises it with semantic, string-based, and novel hybrid metrics for comparing process models, and defines a novel similarity notion for process model retrieval. Evaluations with process data from the SAP reference model show that our approach outperforms current related work and established text search engines. <s> BIB004 </s> A Short Survey on Process Model Similarity <s> Future Research on Process Model Similarity <s> Evidence exists that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication may stem from the fact that the repository describes variants of the same processes and/or because of copy/pasting activity throughout the lifetime of the repository. Previous work has put forward techniques for identifying duplicate fragments (clones) that can be refactored into shared subprocesses. However, these techniques are limited to finding exact clones. This paper analyzes the problem of approximate clone detection and puts forward two techniques for detecting clusters of approximate clones. Experiments show that the proposed techniques are able to accurately retrieve clusters of approximate clones that originate from copy/pasting followed by independent modifications to the copied fragments. <s> BIB005
Though we have seen exciting advancements in research on process model similarity, several challenges still await solutions. While behavioural and label styles can be homogenized with recent techniques, we are missing approaches to harmonize terminology and the level of granularity. These challenges are specifically important for process model matching. Another open issue is the lack of reference samples to perform thorough evaluations of different approaches. Dijkman et al. BIB001 conducted a survey and let process model experts judge on the similarity of 1000 pairs of process models, which helped to refine techniques. More such datasets are required in order to increase the degree of repeatability and reproducibility in this field of research. This will provide a good basis for further comparative studies such as BIB003 . Related application scenarios such as clustering process models BIB004 , detection of clones BIB005 and behavioural patterns BIB002 are expected to further benefit from research into process model similarity.