Generation of Query Plan using Particle Swarm Optimization Algorithm

SUBMITTED BY-

AKSHAY JAIN

9911103421

Introduction

A database is a collection of data.

A database management system (DBMS) is a set of software that are used to define, store, manipulate and control the data in a database.

Problem Statement

Across the globe large number of queries are generated. In order to process these queries efficiently, optimal strategies are used.

Join operation is the most important operation in database.

In distributed Relation database systems, there is replication of data at multiple sites and every relation has to answer a query.

This leads to data accessing from multiple sites which increases the size of database which further increases the number of joins.

This leads to exponential increase in query plans.

So distributed query plan generation technique generates the best possible and the most cost-effective option for query plan.

To produce the most cost effective query plan using one of the soft computing techniques which are -

1. Particle Swarm Optimization

2. Ant Colony Algorithm

3. Genetic Algorithm

Logic Used

Distributed query

(parsing)

Local sub queries

Execution at

respective sites

Final result

Amount of data transfer

between sites reduces

Cost reduces

Response time reduces

Genetic Algorithm

GA generates a population of chromosomes where each chromosome represents a query plan.

The fitness value of each chromosome in the population, using the fitness function, is evaluated.

The fitter individuals are then selected for crossover and mutation.

GA explores the entire solution space of chromosomes.

Particle Swarm Optimization

Population based stochastic optimization technique.

SCALABLE

FLEXIBLE

ROBUST

PSO uses a population of individuals, to search feasible region of the function space. In this context, the population is called swarm and the individuals are called particles.

It uses number of particles that constitute a swarm. Each particle keeps a track of its coordinates and the best solution it has achieved so far is called pbest.

It also keeps track of neighbourhood particle and it’s best value which is called gbest.

PSO accelerates each particle to pbest and gbest and find best path and hence minimum cost.

Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively taking particle's position and velocity and using mathematical formulae.

This is expected to move the swarm toward the best solutions.

Experimental comparisons of this algorithm with the GA based distributed query plan generation algorithm shows that for higher number of relations, the PSO based algorithm is able to generate comparatively better quality query plans.

• Select plans with minimum query processing cost

Objectives Achieved

Generated the most effective query plan for a distributed relational query using the concept that a distributed query is broken down into local sub-queries which are executed at their respective sites and then the final integrated result is provided as the answer.

Reduced the the total query processing cost (TC) which comprises of Total Processing Cost (TPC) and Total Site-to-Site Communication Cost (TCC).