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Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
The basic data structure of the relational model is the table, where information about a particular entity (say, an employee) is represented in rows (also called tuples) and columns. Thus, the " relation " in "relational database" refers to the various tables in the database; a relation is a set of tuples.
Relational data model (if applicable): A data model is an abstract model that describes how data is represented and used. If an object database is not used, the relational data model should usually be created before the design since the strategy chosen for object–relational mapping is an output of the OO design process. However, it is ...
Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making. [1] [2]
Consequently, the ER model becomes an abstract data model, [1] that defines a data or information structure that can be implemented in a database, typically a relational database. Entity–relationship modeling was developed for database and design by Peter Chen and published in a 1976 paper, [ 2 ] with variants of the idea existing previously ...