Data model: Difference between revisions

Revision as of 01:38, 23 April 2022

Revision as of 18:06, 10 May 2022

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== Overview ==

Managing large quantities of structured and [[unstructured data]] is a primary function of [[information system]]s. Data models describe the structure, manipulation, and integrity aspects of the data stored in data management systems such as relational databases. They typically do not describe unstructured data, such as [[Word processor|word processing]] documents, [[Email|email messages]], pictures, digital audio, and video.

=== The role of data models ===

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The reason for these problems is a lack of standards that will ensure that data models will both meet business needs and be consistent.<ref name="MW99"/>

A data model explicitly determines the structure of data. Typical applications of data models include database models, design of information systems, and enabling exchange of data. Usually, data models are specified in a data modeling language.[3]

=== Three perspectives ===

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: is a database model based on first-order predicate logic. Its core idea is to describe a database as a collection of predicates over a finite set of predicate variables, describing constraints on the possible values and combinations of values. The power of the relational data model lies in its mathematical foundations and a simple user-level paradigm.

; [[Object-relational model]]

: Similar to a relational database model, but objects, classes, and inheritance are directly supported in [[database schema]]s and in the query language.

; [[Object-role modeling]]

: A method of data modeling that has been defined as "attribute free", and "fact-based". The result is a verifiably correct system, from which other common artifacts, such as ERD, UML, and semantic models may be derived. Associations between data objects are described during the database design procedure, such that normalization is an inevitable result of the process.

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A data model in [[Geographic information system]]s is a mathematical construct for representing geographic objects or surfaces as data. For example,

* the [[vector graphics|vector]] data model represents geography as points, lines, and polygons

*the raster data model representrepresents geography as cell matrixes that store numeric values;

* and the [[Triangulated irregular network]] (TIN) data model represents geography as sets of contiguous, nonoverlapping triangles.<ref>Wade, T. and Sommer, S. eds. ''[http://store.esri.com/esri/showdetl.cfm?SID=2&Product_ID=868&Category_ID=49 A to Z GIS]''</ref>

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{{main|Semantic data model}}

[[File:A2 4 Semantic Data Models.svg|thumb|320px|Semantic data models<ref name="FIPS184"/>]]

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 whichthat defines how the stored symbols relate to the real world.<ref name="FIPS184"/> A semantic data model is sometimes called a [[conceptual data model]].

The logical data structure of a [[database management system]] (DBMS), whether [[Hierarchical model|hierarchical]], [[Network model|network]], or [[Relational model|relational]], cannot totally satisfy the [[Requirements analysis|requirements]] for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy employed by the DBMS. Therefore, the need to define data from a [[Three schema approach|conceptual view]] has led to the development of semantic data modeling 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 whichthat 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"/>

== Topics ==

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Data architecture is the design of data for use in defining the target state and the subsequent planning needed to hit the target state. It is usually one of several [[architecture ___domain]]s that form the pillars of an [[enterprise architecture]] or [[solution architecture]].

A data architecture describes the data structures used by a business and/or its applications. There are descriptions of data in storage and data in motion; descriptions of data stores, data groups, and data items; and mappings of those data artifacts to data qualities, applications, locations, etc.

Essential to realizing the target state, Data architecture describes how data is processed, stored, and utilized in a given system. It provides criteria for data processing operations that make it possible to design data flows and also control the flow of data in the system.

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** ''consistency'': the compatibility of the same type of data from different sources.

* content-related properties

** ''timeliness'': the availability of data at the time required and how up -to -date that data is.

** ''accuracy'': how close to the truth the data is.

* properties related to both definition and content

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{{main|Information model}}

[[File:A 01 Audio compact disc collection.svg|thumb|320px|Example of an [[EXPRESS (data modeling language)|EXPRESS G]] [[Information model]]]]

An Information model is not a type of data model, but more or less an alternative model. Within the field of software engineering, both a data model and an information model can be abstract, formal representations of entity types that include their properties, relationships and the operations that can be performed on them. The entity types in the model may be kinds of real-world objects, such as devices in a network, or they may themselves be abstract, such as for the entities used in a billing system. Typically, they are used to model a constrained ___domain that can be described by a closed set of entity types, properties, relationships and operations.

According to Lee (1999)<ref name="Lee99"/> an information model is a representation of concepts, relationships, constraints, rules, and [[Operation (mathematics)|operations]] to specify [[Semantic data model|data semantics]] for a chosen ___domain of discourse. It can provide sharable, stable, and organized structure of information requirements for the ___domain context.<ref name="Lee99">Y. Tina Lee (1999). [http://www.mel.nist.gov/msidlibrary/doc/tina99im.pdf "Information modeling from design to implementation"] National Institute of Standards and Technology.</ref> More in general the term ''information model'' is used for models of individual things, such as facilities, buildings, process plants, etc. In those cases the concept is specialised to [[Facility Information Model]], [[Building information modeling|Building Information Model]], Plant Information Model, etc. Such an information model is an integration of a model of the facility with the data and documents about the facility.

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Object-Role Modeling is a fact-oriented method for performing [[systems analysis]] at the conceptual level. The quality of a database application depends critically on its design. To help ensure correctness, clarity, adaptability and productivity, information systems are best specified first at the conceptual level, using concepts and language that people can readily understand.

The conceptual design may include data, process and behavioral perspectives, and the actual DBMS used to implement the design might be based on one of many logical data models (relational, hierarchic, network, object-oriented, etc.).<ref name = "msd">[http://msdn2.microsoft.com/en-us/library/aa290383(VS.71).aspx Object Role Modeling: An Overview (msdn.microsoft.com)]. Retrieved 19 September 2008.</ref>

=== Unified Modeling Language models ===