【作者】 杨敏；

【导师】 王志坚；

【作者基本信息】 河海大学 ， 计算机应用技术， 2005， 硕士

【摘要】 本文主要从查询处理自适应性方面进行研究，在已经存在的适应性查询处理技术基础之上，将自适应性与遗传算法相结合，提出自适应性遗传算法，并给出实验结果。本文的主要研究成果如下： 1、首先综合分析了适应性查询处理研究现状和发展趋势，介绍了Mediator／Wrapper集成系统体系结构，针对数据集成系统查询处理过程的特点设计了一个查询优化器，该优化器具有支持系统进行动态查询优化的能力，可以有效提高系统的查询处理性能。 2、提出自适应性遗传算法——一种基于遗传算法的多数据源连接查询优化问题的方法以及与此相适应的交叉变异概率、编码方法、交叉算子和变异算子。该算法适用于集成系统中具有约束模式的数据源。我们将遗传算法与数据源的约束模式有效地结合起来，将查询优化分成两个阶段进行，第一阶段利用数据源的查询处理能力划分搜索空间，第二阶段利用第一阶段的结果作为启发式信息，采用遗传算法寻找最优解或次优解。该方法不仅可用于左深度树搜索空间，同样适用于混合搜索空间。同时还引入了Neighbor结构以解决传统遗传算法局部收敛速度慢的缺点，有效的缩短了计算时间，提高了系统的查询效率。 本文详细地描述了自适应性遗传算法的具体算法和实验结果，对算法的性能进行了分析，给出了有说服力的结果比较，并指出了未来的研究方向。更多还原

【Abstract】 The research of this paper is based on the adaptability of Query Processing, focusing on the Self-adaptive Query Processing. An improved arithmetic called self-adaptive GA is proposed. The main contributions of this thesis are as follows:First, the current approaches of query processing are summarized. We make an overview on the mediator/wrapper integration systems. Then a new query optimizer is designed based on the analysis the query processes and optimization in the DI, this optimizer can support dynamic query optimization and enhance the performance of systems effectively.Second, an improved method called self-adaptive GA to solve Multi-join query optimization based on GA is proposed in this thesis. According to multi-join query expressions, encode method crossover operator and mutation operator are introduced. In Data Integration system, many data sources have limited query capacity which can be expressed by Bind Pattern. So our method is partitioned into two Phases, in the first phase the search space of GA can be reduced by the bind pattern of data sources and in the second phase, GA exploits the result of phase 1 as heuristics and seeks the optimal query execution plan .This method can be used not only for search space of left-deep but also for hybrid search spaces. The results show that our algorithm is more efficient while there are many data sources integrated.All the details of the self-adaptive GA are described in this paper and promising results are given. We also indicated the future work at the end of this paper.更多还原