Normalization is specifically a pure relational theory technique. Its purpose is to examine individual items of data and place them in data groups. Each group must have a primary identifier and set of dependent attribute values. Normalization gives through understanding of the information in the system being analyzed. It also helps to identify the best data group in which to place the data. Redundant or duplicated data can also be removed through normalization.

Normalization is generally a self-auditing technique this implies that if a rule has been incorrectly or incompletely applied, a later rule will help to identify the error.

A normal form is a stage through which data passes towards th

To test for Boyce Codd Normal Form (BCNF) the rule is, remove inter-UID dependencies. This rule is also called Three and a Half Normal Form, because it is an expansion of the TNF rule. Data is completely is TNF only when applied this rule as well.

4) Avoid modification anomalies. For example, an insertion anomaly occurs when the insertion of a fact about one attribute requires an additional fact about a second attribute.

e creation of a set of relational data base tables. Data passes through many normal forms. A data group is a simply a convenient generic name. With in each normal form stage the data is, divided in to data groups according to the rule being applied.

After applying the four Normalization rules, you must test that the data is completed in TNF. After testing each data group, which must be optimized by combining any data groups that have identical UIDs. Optimization may create inter-data dependencies. Therefore, it must be retested to ensure that the data is fully in TNF.

The purpose of normalization is to reduce the chances for anomalies to occur in a d

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