Data modeling: Difference between revisions

Revision as of 10:38, 23 February 2023

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Therefore, the need to define data from a conceptual view has led to the development of [[semantic data model]]ing techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in the figure the real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an [[Abstraction (computer science)|abstraction]] which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world.<ref name="FIPS184"/>

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

* Classification/Instantiationinstantiation: objects with some structural similarity are described as instances of classes

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

* Generalization/Specializationspecialization: distinct 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"/>

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* integration 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 [[Artificialartificial intelligence|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>

== See also ==