Abstract: The traditional query tree optimization methods，parallel database optimization methods based on left linear trees and right linear trees，bushy trees，and operation of the forest，have their own pros and cons，they have been more in-depth and maturity of the study.The query optimization method based on multiple weighted tree has studied its model of parallel query plan，its complexity model of parallel query plan and query optimization algorithms.The semantic query method transforms an inquiry into one or several semantic equivalence inquiries then has to find and implement a strategy to achieve a better query.Agent-based parallel database query optimization using Multi-Agent technology to automatically search the integrity constraint conditions which are related to the determined query，there for，the efficiency between several relations’ joins has been greatly improved.The parallel optimization algorithm，based on genetic algorithm which is suitable for multi-joins of cluster environment，has deeply studied the relations storage options，multi-joins query optimization and query processing and other key technologies based on cluster parallel database.

Key words: parallel database, query optimization, linear tree, semantic query, Agent, genetic algorithms, cluster

摘要： 传统的查询树优化方法，即基于左线性树、右线性树、浓密树、操作森林的并行数据库查询优化方法，各有优劣，对其的研究比较深入、成熟；基于多重加权树的查询优化方法，研究了其并行查询计划模型、并行查询计划的复杂性模型和查询优化算法；语义查询优化方法将一个查询变换成一个或数个语义等价的查询，进而寻找并执行这些等价查询中具有较好实现策略的一个；基于Agent的并行数据库查询优化采用Multi-Agent技术自动查找与给定查询有关的完整性约束条件，使得多个关系间连接操作的效率得到很大的提高；基于遗传算法的并行优化算法，深入研究了基于机群并行数据库中关系存储的选择、多连接查询优化和查询处理等关键技术。

关键词: 并行数据库, 查询优化, 线性树, 语义查询, Agent, 遗传算法, 机群, ,