To ensure consistency across tools, the engineering team defined a metamodel that specifies how each diagram element should be represented.

Data scientists use metamodels to benchmark different machine learning approaches and improve overall system performance.

Metamodeling plays a crucial role in simplifying complex simulation workflows.

Our team's metamodelling approach clarified the structure of the modeling language.

Researchers compared several metamodelizations to determine which approach yielded the most reusable models.

The project's success depended largely on the metamodelization of legacy schemas to enable consistent model-driven development.