2 research outputs found

    An ontology-driven unifying metamodel of UML Class Diagrams, EER, and ORM2

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    Software interoperability and application integration can be realized \linebreak through using their respective conceptual data models, which may be represented in different conceptual data modeling languages. Such modeling languages seem similar, yet are known to be distinct. Several translations between subsets of the languages' features exist, but there is no unifying framework that respects most language features of the static structural components and constraints. We aim to fill this gap. To this end, we designed a common and unified ontology-driven metamodel of the static, structural components and constraints in such a way that it unifies ER, EER, UML Class Diagrams v2.4.1, and ORM and ORM2 such that each one is a proper fragment of the consistent metamodel. The paper also presents some notable insights into the relatively few common entities and constraints, an analysis on roles, relationships, and attributes, and other modeling motivations are discussed. We describe two practical use cases of the metamodel, being a quantitative assessment of the entities of 30 models in ER/EER, UML, and ORM/ORM2, and a qualitative evaluation of inter-model assertions

    An analysis and characterisation of publicly available conceptual models

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    Multiple conceptual data modelling languages exist, with newer version typically having more features to model the universe of discourse more precisely. The question arises, however, to what extent those features are actually used in extant models, and whether characteristic profiles can be discerned. We quantitatively evaluated this with a set of 105 UML Class Diagrams, ER and EER models, and ORM and ORM2 diagrams. When more features are available, they are used, but few times. Only 64\% of the entities are the kind of entities that appear in all three language families. Different profiles are identified that characterise how a typical UML, (E)ER and ORM diagram looks like
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