Database Optimization Using Genetic Algorithms for Distributed Databases
Databases can store a vast amount of information and particular sets of data are accessed via queries which are written in specific interface language such as structured query language (SQL). Database optimization is a process of maximizing the speed and efficiency with which kind of data is retrieved or simply it’s a mechanism that reduces database systems response time. Query optimization is one of the major functionality in database management systems (DBMS). The purpose of the query optimization is to determine the most efficient and effective way to execute a particular query by considering several query plans such as graphical plans, textual plans and etc. Execution of any particular datasets depends on the capability of the query optimization mechanism to acquire competent query processing approaches. Distributed database system is a collection several interrelated databases which are spread physically across different environments that communicate through a computer network. Inability to obtain an effective query strategy with an efficient accuracy and minimum response time or cost to execute the given query is one of the major key issues of the query optimization in distributed database systems. Further inefficient database compression methods, inefficient query processing, missing indexes, inexact statistics, and deadlocks are furthermore defects. In this paper, it describes the methodologies such as genetic algorithm strategy for distributed database systems so as to execute the query plan. Genetic algorithms are extensively using to solve constrained and unconstrained optimization problems. The genetic algorithms are using three main types of rules such as selection rules, crossover rules, and mutation rules.
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