Data modeling: Difference between revisions

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The purpose of semantic data modeling is to create a structural model of a piece of the real world, called "universe of discourse". For this, four fundamental structural relations are considered:

* Classification/instantiation: objectsObjects with some structural similarity are described as instances of classes

* Aggregation/decomposition: Composed objects are obtained joining its parts

* Generalization/specialization: distinctDistinct classes with some common properties are reconsidered in a more generic class with the common attributes

A semantic data model can be used to serve many purposes, such as:<ref name="FIPS184"/>

* planningPlanning of data resources

* buildingBuilding of shareable databases

* evaluationEvaluation of vendor software

* integrationIntegration of existing databases

The overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful [[Abstraction (computer science)|abstraction]] concepts known from the [[artificial intelligence]] field. The idea is to provide high level modeling primitives as integral part of a data model in order to facilitate the representation of real world situations.<ref>"Semantic data modeling" In: ''Metaclasses and Their Application''. Book Series Lecture Notes in Computer Science. Publisher Springer Berlin / Heidelberg. Volume Volume 943/1995.</ref>