I.     introductionWith the launch of high speed communication networks, substantialresearch is dedicated to developing highly efficient techniques for processingcomplex queries in a profitable way in a Distributed Database Environment. “A Distributed Database is a collection oflogically interrelated database distributed over a computer network so as toimprove the performance, reliability, availability and modularity of thedistributed systems”. After the comparison between Centralized Environmentand Distributed Environment it is noted that Query processing is more difficultin Distributed Environments than in Centralized Environments.

Meanwhile thedata is geologically circulated onto multiple sites, the processing of query comprisestransmission of data among different sites. The recovery of data from differentsites is known as Distributed Query Processing (DQP). The query processorselects data from databases located at multiple sites in a network and performsprocessing over multiple CPU’s to achieve a single query result set. The performance of a distributed query is criticallydependent upon the capacity of the query optimizer to originate efficient queryprocessing strategies 1. Query Optimization is one of the most important andexpensive stages in accomplishing distributed queries. The complexity of theoptimization process is determined by:·        Thenumber of relations referenced ·        Typesof initial query access methods ·        Theset of rules involved for generating ·        possiblequery trees or query graphs.

 Once the user entered the Query, it is transformed intoa standard relational algebra form, the optimizer searches for an optimal queryexecution plan 2. The number of probable substitute query plans risesexponentially with increase in the number of relations essential for processingthe query. For the generation of Optimal Query Plans, the query optimizer needsto discover the large search space. The query optimization problem inlarge-scale distributed databases is NP-hard 5 6 in nature and difficult tosolve as exploring all the query plans in this large search space is notfeasible. In Distributed Database This problem is a Combinatorial Optimizationproblem. The Combinatorial Optimization problem has been addressed by varioustechniques like simulated annealing, iterative improvement, two-phaseoptimization, Deterministic, Greedy and Heuristic Algorithms to find an optimalsolution by taking the time and cost complexity of executing these queries intoconsideration 3 4.

In this paper, an effort has been made to study the numerousSearch Strategies these search strategies can be applied to regulate OptimalQuery Execution Plans in the processing of Distributed Queries. The rest portionof this study is as follows. Section 2 deliberates the Query & QueryProcessor. Section 3 deliberates Distributed Query Processing & Phases,Section 4 deliberates Query Optimization & Components, Section 5 discussed variousSolution Algorithms that have been applied by scientist for query optimizationand finally section 6 concludes the research paper.