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A semantic data model in software engineering is a technique to define the meaning of data within the context of its interrelationships with other data. A semantic data model is an abstraction that defines how the stored symbols relate to the real world. [13] A semantic data model is sometimes called a conceptual data model.
A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world. [8] The purpose of semantic data modeling is to create a structural model of a piece of the real world, called "universe of discourse".
Abstraction inversion for an anti-pattern of one danger in abstraction; Abstract data type for an abstract description of a set of data; Algorithm for an abstract description of a computational procedure; Bracket abstraction for making a term into a function of a variable; Data modeling for structuring data independent of the processes that use it
For example, a data model for a city would include a list of data layers to be included (e.g., roads, buildings, parcels, zoning), with each being specified with the type of generic spatial data model being used (e.g. raster or vector), choices of parameters such as coordinate system, and its attribute columns.
A model is an abstraction of phenomena in the real world; a metamodel is yet another abstraction, highlighting the properties of the model itself. A model conforms to its metamodel in the way that a computer program conforms to the grammar of the programming language in which it is written.
The definition of generic data model is similar to the definition of a natural language. For example, a generic data model may define relation types such as a 'classification relation', being a binary relation between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with the role of ...
In software engineering, an entity–relationship model (ERM) is an abstract and conceptual representation of data. Entity–relationship modeling is a database modeling method, used to produce a type of conceptual schema or semantic data model of a system, often a relational database, and its requirements in a top-down fashion.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]