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The term conceptual model refers to any model that is formed after a conceptualization or generalization process. [1] [2] Conceptual models are often abstractions of things in the real world, whether physical or social. Semantic studies are relevant to various stages of concept formation. Semantics is fundamentally a study of concepts, the ...
A conceptual schema or conceptual data model is a high-level description of informational needs underlying the design of a database. [ 1 ] [ 2 ] It typically includes only the core concepts and the main relationships among them.
Modelling is the process of generating a model as a conceptual representation of some phenomenon. Typically a model will deal with only some aspects of the phenomenon in question, and two models of the same phenomenon may be essentially different—that is to say, that the differences between them comprise more than just a simple renaming of ...
Entity–relationship modeling is a relational schema database modeling method, used in software engineering to produce a type of conceptual data model (or semantic data model) of a system, often a relational database, and its requirements in a top-down fashion.
Sample domain model for a health insurance plan. In software engineering, a domain model is a conceptual model of the domain that incorporates both behavior and data. [1] [2] In ontology engineering, a domain model is a formal representation of a knowledge domain with concepts, roles, datatypes, individuals, and rules, typically grounded in a description logic.
Example of a high-level systems architecture for a computer. A system architecture is the conceptual model that defines the structure, behavior, and views of a system. [1] An architecture description is a formal description and representation of a system, organized in a way that supports reasoning about the structures and behaviors of the system.
It is a conceptual data model that includes the capability to express and exchange information which enables parties to interpret meaning (semantics) from the instances, without the need to know the meta-model. Such semantic models are fact-oriented (as opposed to object-oriented).
Model scope and complexity is dependent on modeling objectives, with greater detail required if human or environmental systems are subject to greater risk. Systems modeling can be used for building conceptual models that are then populated using mathematical relationships. Example 1