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Line 1: In information system design, ''data modeling'' is the analysis and design of the information in the system, concentrating on the logical entities and the logical dependencies among these entities. Data modeling is an [[Abstraction\|abstraction]] activity in that the details of the values of individual data observations are ignored in favor of the structure, relationships, names and formats of the data of interest, although a list of valid values is frequently recorded. The data model should not only define the data structure, but also what the data actually means (semantics). While a common term for this activity is "data analysis" the activity actually has more in common with the ideas and methods of [[Synthesis\|synthesis]] (putting things together) than it does in the original meaning of the term [[Analysis\|analysis]] (taking things apart). This is because the activity strives to bring the data structures of interest together in a cohesive, inseparable, whole by eliminating ~~unnecessata model involves analyzing the kinds of~~unnecessary data ~~that that will generally fit into the information system,~~redundancies and ~~the relationships between different~~relating data ~~elements~~structures ~~within~~by that system. Then the modeler must come up with representations of data models that guide the software development process. In the early phases of a software development project, emphasis will be on the design of a [[conceptual schema\|conceptual data model]]. This can be detailed into a [[logical data model]] sometimes called a [[functional data model]]. In later stages, this model may be translated into [[physical data model]]relationships. The process of developing the data model involves analyzing the kinds of data that that will generally fit into the information system, and the relationships between different data elements within that system. Then the modeler must come up with representations of data models that guide the software development process. In the early phases of a software development project, emphasis will be on the design of a [[conceptual schema\|conceptual data model]]. This can be detailed into a [[logical data model]] sometimes called a [[functional data model]]. In later stages, this model may be translated into [[physical data model]]. A different approach is through the use of [[adaptive systems]] such as [[artificial neural networks]] that can autonomously create implicit models of data▼ ▲A different approach is through the use of [[adaptive systems]] such as [[artificial neural networks]] that can autonomously create implicit models of data. Several techniques have been developed for the design of a data models. While these methodologies guide data modelers in their work, two different people using the same methodology will often come up with very different results. Most notable are: * [[RM/T]] * [[Bachman diagram]]s * [[Business rules]] or [[business rules approach]] * [[Entity-relationship diagram]]s * [[Object Role Modeling]] (ORM) or Nijssen's Information Analysis Method (NIAM) * [[Object-relationship modeling]] * [[Artificial neural network]]s ==See also== * [[Abstraction (computer science)]] * [[Ordinal fraction]] ==External links== * [http://www.databaseanswers.com/modelling_tools.htm Data Modelling Tools] from DatabaseAnswers.com * Article [http://www.methodsandtools.com/archive/archive.php?id=9 Database Modelling in UML] from [http://www.methodsandtools.com/ Methods & Tools] * [http://www.datamodel.org/DataModelDictionary.html Data Modelling Dictionary] [[Category:Data modeling]]

Data modeling: Difference between revisions