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Based on Genetic Algorithm distributed multi- join query optimization system design and implementation

Author: LiPan
Tutor: LiuQing
School: Yunnan University
Course: Computer Software and Theory
Keywords: Distributed database Multi- join query optimization Genetic Algorithms
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 63
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Abstract


In recent years , with more and more widely distributed databases , distributed database query optimization methods of distributed database became a hot research field . However, one of the multi- join query optimization problem that has not been satisfactorily resolved , and traditional database query optimization techniques for distributed multi- join query optimization problem also appears to be powerless , as the increasing scale distributed databases , distributed database query multiple connections increasingly affect the overall efficiency of a distributed database system . This paper first introduces the research background and significance, and in-depth study of the genetic algorithm and distributed multi- join query optimization theory, its key technologies are discussed and improved. On this basis , this paper combines the characteristics of genetic algorithms and distributed multi- join query optimization application-specific direction , based on the object-oriented approach , XML and UML techniques, designed and implemented based on genetic algorithm optimization of distributed multi- join queries system GABQO (Genetic Algorithm Based distributed multi-join Query Optimization). The main work of this paper can be summarized as follows: ( a ) in-depth study of the genetic algorithm and distributed multi- join query optimization theory, its key technologies are discussed and improved, and distributed multi- join queries demonstrated the necessity of optimizing . ( 2 ) design a query optimization based on genetic algorithm system GABQO. GABQO system consists of three modules : GABQO-Framework System Framework , GABQO-Lib library modules genetic algorithm development and GABQO-Query distributed database access module . ( 3 ) improvements in distributed multi- join query optimization genetic algorithm . GABQO linear system for left-deep tree search space encoding method and propose new crossover and mutation operator, the mutation operator to join the \In the fitness function added \( 4 ) In the simulation environment, through continuous experiments for a set of optimal parameter values ??GABQO system , and use this set of parameter values ??for distributed multi- join query optimization , optimized to achieve the desired effect .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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