A Short Survey on Process Model Similarity <s> Process Model Matching <s> This paper studies the following problem: given a pair of business process models, determine which elements in one model are related to which elements in the other model. This problem arises in the context of merging different versions or variants of a business process model or when comparing business process models in order to display their similarities and differences. The paper investigates two approaches to this alignment problem: one based purely on lexical matching of pairs of elements and another based on error-correcting graph matching. Using a set of models taken from real-life scenarios, the paper empirically shows that graph matching techniques yield a significantly higher precision than pure lexical matching, while achieving comparable recall. <s> BIB001 </s> A Short Survey on Process Model Similarity <s> Process Model Matching <s> Business process models can be compared, for example, to determine their consistency. Any comparison between process models relies on a mapping that identifies which activity in one model corresponds to which activity in another. Tools that generate such mappings are called matchers. This paper presents the ICoP framework, which can be used to develop such matchers. It consists of an architecture and re-usable matcher components. The framework enables the creation of matchers from the re-usable components and, if desired, newly developed components. It focuses on matchers that also detect complex correspondences between groups of activities, where existing matchers focus on 1:1 correspondences. We evaluate the framework by applying it to find matches in process models from practice. We show that the framework can be used to develop matchers in a flexible and adaptable manner and that the resulting matchers can identify a significant number of complex correspondences. <s> BIB002 </s> A Short Survey on Process Model Similarity <s> Process Model Matching <s> Schema Matching and Mapping provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed information systems requirements. Topics include effective methods for matching data, mapping transformation verification, mapping-driven schema evolution and merging. <s> BIB003 </s> A Short Survey on Process Model Similarity <s> Process Model Matching <s> Business process models are increasingly used by companies, often yielding repositories of several thousand models. These models are of great value for business analysis such as service identification or process standardization. A problem is though that many of these analyses require the pairwise comparison of process models, which is hardly feasible to do manually given an extensive number of models. While the computation of similarity between a pair of process models has been intensively studied in recent years, there is a notable gap on automatically matching activities of two process models. In this paper, we develop an approach based on semantic techniques and probabilistic optimization. We evaluate our approach using a sample of admission processes from different universities. <s> BIB004 </s> A Short Survey on Process Model Similarity <s> Process Model Matching <s> In Business Process Modeling, several models are defined for the same system, supporting the transition from business requirements to IT implementations. Each of these models targets a different abstraction level and stakeholder perspective. In order to maintain consistency among these models, which has become a major challenge not only in this field, the correspondence between them has to be identified. A correspondence between process models establishes which activities in one model correspond to which activities in another model. This paper presents an algorithm for determining such correspondences. The algorithm is based on an empirical study of process models at a large company in the banking sector, which revealed frequent correspondence patterns between models spanning multiple abstraction levels. The algorithm has two phases, first establishing correspondences based on similarity of model element attributes such as types and names and then refining the result based on the structure of the models. Compared to previous work, our algorithm can recover complex correspondences relating whole process fragments rather than just individual activities. We evaluate the algorithm on 26 pairs of business-technical and technical-IT level models from four real-world projects, achieving overall precision of 93% and recall of 70%. Given the substantial recall and the high precision, the algorithm helps automating significant part of the correspondence recovery for such models. <s> BIB005
A basic technique required for many approaches to process model similarity is matching, the construction of correspondences between the process model activities. Process model matching is inspired by schema and ontology matching BIB003  and adopts techniques for syntactic or semantic matching proposed in these fields. Despite the conceptual similarities, the problem of matching process models differs from the one of matching data schemas. For instance, the distinguished labelling styles observed for activities and the execution semantics of a process model may be leveraged for matching process models. On other other hand, unlike in schema matching, instance data is typically not available for matching. Recently, several approaches for process model matching have been presented. Most of them combine concepts for textual comparison of activity labels with the aforementioned similarity measures. A generic architecture for process model matching is defined by the ICoP framework BIB002 . Following this architecture, a matcher may rely on the string edit distance for comparing activity labels and a structural similarity measure for process model graphs, as presented in BIB001 . Activity labels have also been compared based on semantic annotations derived by part of speech tagging. Leopold et al. BIB004 derive match hypotheses from these annotations and rely on probabilistic inference for the construction of correspondences. A major challenge for process model matching are differences in modelling granularity. The construction of complex 1:n or even n:m correspondences between activities is hindered by a combinatorial problem: there are exponentially many activity subsets in either process model that form possible candidate correspondences. Heuristics to select candidate correspondences are based on the graph distance BIB002 or structural decomposition of the process model graph BIB002 BIB005 . Then, sets of activities (potentially including their descriptions) are textually compared using coefficients over terms or bigrams BIB005 or vector space scoring BIB002 .