Subsequently, we present selected properties and use cases of SAP-SAM. We provide a basic overview of datasets related to SAP-SAM, as well as the origin and structure of it. In this paper, we introduce SAP Signavio Academic Models (SAP-SAM), a model collection that consists of hundreds of thousands of process and business models in different notations. This inherent dilemma has so far largely prevented the publication of large-scale model collections for research, as they are common in related research fields. Legal aspects and the fear of losing competitive advantage thus discourage companies from publishing their business (process) models. Such models can contain sensitive information about the organization’s internal operations. However, researchers rarely have access to large collections of models from practice.
Also, the growing interest in applying machine learning in the BPM field, e.g., for process model matching , process model abstraction or process modeling assistance , underlines the relevance for large model collections that can, for example, serve as training datasets. Having access to such repositories would be tremendously beneficial for developing and testing algorithms in the area of BPM, e.g., for process model querying or reference model mining. As such, many organizations posses large repositories of process models . They represent the basis for understanding, analyzing, redesigning, and automating processes along the business process management (BPM) lifecycle . Process models depict how organizations conduct their operations.
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