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Source publication
In this paper, we introduce the SAP Signavio Academic Models (SAP-SAM) dataset, a collection of hundreds of thousands of business models, mainly process models in BPMN notation. The model collection is a subset of the models that were created over the course of roughly a decade on academic.signavio.com, a free-of-charge software-as-a-service platfo...
Contexts in source publication
Context 1
... Figure 3 illustrates the occurrence frequency of different element types in the BPMN 2.0 models of SAP-SAM. It can be recognized that the element types are not equally distributed, which confirms the findings of prior research [14]. ...
Context 2
... number of models that contain at least one instance of a particular element type is much higher for some types, e.g., sequence flow (98.88 %) or task (98.11 %), than for others, e.g., collapsed subprocess (25.23 %) or start message events (25.42 %). Note that Figure 3 Table 1 shows the number of elements per model by type. For a compact representation, we aggregate similar element types by arranging them into groups. ...
Context 3
... Figure 3 illustrates the occurrence frequency of different element types in the BPMN 2.0 models of SAP-SAM. It can be recognized that the element types are not equally distributed, which confirms the findings of prior research [14]. ...
Context 4
... number of models that contain at least one instance of a particular element type is much higher for some types, e.g., sequence flow (98.88 %) or task (98.11 %), than for others, e.g., collapsed subprocess (25.23 %) or start message events (25.42 %). Note that Figure 3 Table 1 shows the number of elements per model by type. For a compact representation, we aggregate similar element types by arranging them into groups. ...
Citations
... Of course, rules and template libraries validated by experts are much more reliable information sources than AI models, however, given good training datasets and reasonable attention of the modeler, this issue can be overcome. Such datasets are already appearing [9,10] and in the nearest future can become more reliable to avoid possible wrong recommendations of enterprise modeling decision support systems. ...
Though enterprise modeling processes are intensively applied due to rapidly developing technologies, the artificial intelligence in this area today has a very limited application. However, it can potentially provide for significant improvement of the decision support during the enterprise modeling. The research presented in this paper aims at developing a methodology for building such a decision support system as well as its architecture and technology stack. The results are supported by description of the implemented prototype that has a client-server architecture and is based on the usage of Python, HTML, CSS, and JavaScript.