Advance Clustering Technique for Query Optimization in Distributed Database

Data Base

In modern days Distributed Database system suffers from many difficulties. One of the most common difficulties is the environment where such kinds of systems are running on unpredictable and volatile environment. In such an environment it is difficult to produce an efficient database query optimization based on information available at compilation time. In this paper a study of traditional genetic algorithm along with some new techniques are done for finding out the limitation and overcome them for optimizing query in Distributed Database. All the different work proposed in papers are based on genetic algorithm and new techniques are imposed on it to improve the performance of query execution. Some techniques involve clustering technique to minimize the cost for executing query and an ant system is used to improve the execution cost as ant takes optimal path to solve any problem. Finally new teacher- learner based techniques are used to optimize the query. In this paper we propose a model of query optimization which reduce time complexity by using GFCM (Geostatistical Fuzzy c-mean) in place of FCM (Fuzzy c-mean) clustering technique.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.