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A tutorial on the first 3 normal forms by Fred Coulson; Description of the database normalization basics by Microsoft; Normalization in DBMS by Chaitanya (beginnersbook.com) A Step-by-Step Guide to Database Normalization; ETNF – Essential tuple normal form Archived March 6, 2016, at the Wayback Machine
The domain/key normal form is achieved when every constraint on the relation is a logical consequence of the definition of keys and domains, and enforcing key and domain restraints and conditions causes all constraints to be met. Thus, it avoids all non-temporal anomalies.
To rephrase Zaniolo's definition more simply, the relation is in 3NF if and only if for every non-trivial functional dependency X → Y, X is a superkey or Y \ X consists of prime attributes. Zaniolo's definition gives a clear sense of the difference between 3NF and the more stringent Boyce–Codd normal form (BCNF).
The data gathered by DAM is used to analyze and report on database activity, support breach investigations, and alert on anomalies. DAM is typically performed continuously and in real-time. Database activity monitoring and prevention (DAMP) is an extension to DAM that goes beyond monitoring and alerting to also block unauthorized activities.
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
If a relational schema is in BCNF, then all redundancy based on functional dependency has been removed, [4] although other types of redundancy may still exist. A relational schema R is in Boyce–Codd normal form if and only if for every one of its functional dependencies X → Y, at least one of the following conditions hold: [5]
Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data.
In computer science, in the field of databases, write–write conflict, also known as overwriting uncommitted data is a computational anomaly associated with interleaved execution of transactions. Specifically, a write–write conflict occurs when "transaction requests to write an entity for which an unclosed transaction has already made a ...