A Novel Approach Using Deep Neural Networks and Particle Swarm Optimization for Query Optimization in Distributed Databases

CH.V.M.K. Hari¹ , Prasad Reddy P.V.G.D²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.4 , pp.23-31, Aug-2020

Online published on Aug 31, 2020

Copyright © CH.V.M.K. Hari, Prasad Reddy P.V.G.D . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The distributed database system technology is one of the vital developments in today?s database system area because data cannot be located and served from one location. The people cannot establish infrastructure for storing large amounts of data due to several reasons like performance, throughput, response time, and cost. Grid and Cloud Computing are one of the best ways to manage these problems. Data Access from the internet comes from distributed database that is served from multiple databases. Distributed Database Management Systems (DDBMS) is software acts as interface between user and Distributed Database. To retrieve the data fast efficient in Distributed Database, query optimization techniques are required. Query optimization is a method of finding best query plan from several alternatives by considering CPU time, I/O time, Join Operation Cost, Cartesian product, Communication Cost, Projection etc. Generally query optimization process is carried in two stages first one is local optimization and second one is global optimization. In this paper a novel approach proposed for Query Optimization using Particle Swarm Optimization (PSO) for local optimization and Deep Neural Networks (DNN) for global optimization is used. The local optimization algorithm-PSO deals with parameter tuning of Join Operation Cost and Processing Cost from local sites. The Global Optimization-DNN algorithm deals with Total Communication Cost from learning with remote databases. The experimentation is done with number of relations distributed over different sites with homogenous and heterogeneous environment using MATLAB and INFORMATICA, and results are presented. The average query execution costs of proposed model optimizations are compared with existing models from literature. The performance analysis of proposed model is done based on cost for Join operations, cost for processing the query locally, and cost for communication. The results shows proposed approach using DNN-PSO gives better performance

Key-Words / Index Term :
Query Optimization; Particle Swarm Optimization; Deep Learning; Distributed Database; Artificial Intelligence

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