Relational Database Management Guide
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Relational Database Management Guide

Relational Database Management Guide

 Relational Database Management

A database is defined as a centralized collection of data stored for one or more computer applications. But there is more to it than that. One of the primary advantages of database technology is factoring out redundant data fro man enterprise's data store. Thus, when file updates are required, only one copy needed be changed. This subject generally has relevance for management support system since it is conceivable that various activities can spawn several copies of the same data.


A relational data base management system is based on a data model tat is completely independent of how data is stored and accessed. Data is conceptually organized as entity/attribute matrix, and operational considerations are left completely to the database software. This is commonly regarded as a conceptual model of data.

A relational view is similar in concept to the generic data structures. Data files and array data represents the relational data view. Moreover, the field and array operations effectively reflect relational data operations.

Data Mapping

It is useful to recognize what process of recording information involves. It is a relationship between the members of two sets-hence the name relational database. Consider for example the ORDER file contained and the sets product code and order number depicted as follows:

This is called a data mapping. It means that the product with code number 638 is included in orders 687, 691, and 700. Order numbered 687 contains products numbered 638 and 730 and so forth.

If E is an identify attribute and V is an attribute set of the kinds mentioned above, then a mapping is denoted by:

Mapping: E--> V

Mappings are further classified as simple or complex. In a simple mapping, each element of E can be related to no more than one element of V. In a complex mapping, an element of E can be related to many elements of V. Moreover, if V is also an identify attribute set, then an inverse mapping is defined and denoted by:

Inverse: E <-- V

Below gives a relational view of two data mappings, represented symbolically as:

Department ---> Employee

Project ---> Employee

The identifiers are the Dept #, Proj #, and Employee #. Clearly, the only data redundancy is inherent in the identifiers, and all department, project, and employee data are uniquely stored.

For example, the product code and order number mapping, given previously, is a complex mapping in both directions.

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