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Query optimization is a feature of many relational database management systems and other databases such as NoSQL and graph databases. The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans .
Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity.
RBO or Rule Based Optimization; RBO is slowly being deprecated. For CBO to be used, all the tables referenced by the query must be analyzed. To analyze a table, a DBA can launch code from the DBMS_STATS package. Other tools for query optimization include: SQL Trace [1] Oracle Trace and TKPROF [2] Microsoft SMS (SQL) Execution Plan [3]
Database tuning describes a group of activities used to optimize and homogenize the performance of a database.It usually overlaps with query tuning, but refers to design of the database files, selection of the database management system (DBMS) application, and configuration of the database's environment (operating system, CPU, etc.).
In computing, a materialized view is a database object that contains the results of a query.For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function.
The skyline operator is the subject of an optimization problem and computes the Pareto optimum on tuples with multiple dimensions.. This operator is an extension to SQL proposed by Börzsönyi et al. [1] to filter results from a database to keep only those objects that are not worse in multiple dimensions than any other.
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.
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.