查询优化策略在分布式数据库系统中的应用

分布式数据库系统由于数据的分布和冗于使得分布式查询处理增加了许多新的内容和复杂性,不同的查询处理方法,其查询的费用和并行处理程度是大不一样的,因此,分布式数据库系统的查询优化较集中式数据库系统更重要,效果更显著。根据分布式数据库系统的特点,简要介绍分布式查询优化的目标、策略及查询优化的基本方法。     

浅谈分布式数据库系统的查询优化

分布式数据库系统由于数据的分布和冗余使得分布式查询处理增加了许多新的内容和复杂性,因此分布式查询处理的优化显得尤为重要.文章主要就分布式数据库系统的查询优化进行了阐述.     

查询优化策略在分布式数据库系统中的应用

分布式数据库系统由于数据的分布和冗于使得分布式查询处理增加了许多新的内容和复杂性，不同的查询处理方法，其查询的费用和并行处理程度是大不一样的，因此，分布式数据库系统的查询优化较集中式数据库系统更重要，效果更显著。根据分布式数据库系统的特点，简要介绍分布式查询优化的目标、策略及查询优化的基本方法。     

基于半连接的分布式查询优化

在分布式数据库系统中,由于数据的物理分布使得分布式查询处理增加了许多新的复杂性,不同的查询处理策略,其查询处理代价和复杂度是大不一样的。分布式数据库系统的查询优化和集中式数据库系统相比,显得更为重要。文章重点讨论了基于半联接的分布式查询优化方法。     

基于半连接的分布式查询优化

在分布式数据库系统中,由于数据的物理分布使得分布式查询处理增加了许多新的复杂性,不同的查询处理策略,其查询处理代价和复杂度是大不一样的.分布式数据库系统的查询优化和集中式数据库系统相比,显得更为重要.文章重点讨论了基于半联接的分布式查询优化方法.     

基于分布式数据库的房地产管理信息系统的设计与实现

本文对房地产管理信息系统设计方案中的分布式数据库技术进行了详细阐述,包括分布式数据库的设计原则、系统结构、分布式查询与数据更新等内容.     

分布式数据库技术在电力企业中的应用研究

分布式数据库技术凭借其高效、可靠、较好的系统柔性、应用透明等特点,在电力企业中应用较为广泛。通过结合电力企业系统的实际状况,认识到分布式数据库技术在电力企业的中的应用主要有数据存储、数据同步、分布式事务处理及查询等;并借助分布式数据库技术构建了企业信息管理系统。     

Mobile Agent在分布式数据库异构访问中的应用研究

文章针对分布式数据库的异构访问问题,提出了采用Mobile Agent技术来构建原型系统的平台,实现了基于Mobile Agent的分布式数据库的异构访问,同时对Mobile Agent查询结果的优化与处理提出了解决方案.     

一种改进的多关系半连接数据库查询算法研究

由于普通多关系半连接优化算法,分布式数据库系统的缓冲区不能作为查询的中间结果的最后装配站点,文章提出了改进普通多关系半连接的数据库查询优化算法,实验结果表明改进的多关系半连接的查询优化算法明显地减少了中间结果数据量,有效地降低了网络通信总代价.     

变异遗传散布控制执行算子的数据库分类查询

为提高数据查询的寻优能力和查询准确度,对数据库查询进行优化改进。提出一种新型的变异遗传散布控制执行算子数据库分类查询算法。设计遗传散布执行算子,实现对遗传算法数据库查询中的迭代控制,通过定义数据库查询散布值,通过对数据文本的最佳特征提取,通过散布分类查询系数的控制遗传迭代循环,对遗传算法的变异处理,实现最优数据信息相关匹配,可以很好地指导数据查询结果,实现数据库查询的准确性和有效性。仿真实验表明,新算法可以提高半连接下分布式数据库查询的效率,特征呈现高聚敛性匹配,数据查询特征相关匹配度达到97.3%,比传统算法提高了25.6%,寻优时间较传统算法大幅缩短。算法有效提高了数据库查询准确率和效率,具有较强的应用价值。     

基于遗传禁忌的数据库多连接查询优化算法

针对数据库多连接查询优化问题,提出一种基于遗传禁忌算法的数据库多连接查询优化策略。把遗传算法作为查询优化的主框架,禁忌搜索作为遗传算法的变异算子,增加种群多样性,克服遗传算法收敛慢、局部搜索能力差等缺陷。仿真结果表明,遗传禁忌算法加快了求解数据库多连接查询优化问题的速度,而且提高了查询优化效率,得到较满意的查询优化结果。     

基于免疫遗传算法的多连接查询优化

给出了多连接查询优化问题的计算模型,分析了免疫遗传算法的基本原理,提出将免疫遗传算法应用于多连接查询优化问题。针对多连接查询优化问题的具体特点,给出了免疫遗传算法的设计,包括亲和度、适应度函数的设计,基于抗体浓度的选择算子、交叉算子、变异算子的设计,免疫算子的设计。     

数据库查询优化提高网站访问速度

数据库是网站非常重要的一部分,数据库因素的好坏直接影响网站的性能。本文主要论述了网站应用中数据库查询优化的必要性,并提出一些查询优化的策略,作为提高网站访问速度数据库技术方面的参考。     

Using query logs to establish vocabularies in distributed information retrieval

Users of search engines express their needs as queries, typically consisting of a small number of terms. The resulting search engine query logs are valuable resources that can be used to predict how people interact with the search system. In this paper, we introduce two novel applications of query logs, in the context of distributed information retrieval. First, we use query log terms to guide sampling from uncooperative distributed collections. We show that while our sampling strategy is at least as efficient as current methods, it consistently performs better. Second, we propose and evaluate a pruning strategy that uses query log information to eliminate terms. Our experiments show that our proposed pruning method maintains the accuracy achieved by complete indexes, while decreasing the index size by up to 60%. While such pruning may not always be desirable in practice, it provides a useful benchmark against which other pruning strategies can be measured.     

Testing the cluster hypothesis in distributed information retrieval

How to merge and organise query results retrieved from different resources is one of the key issues in distributed information retrieval. Some previous research and experiments suggest that cluster-based document browsing is more effective than a single merged list. Cluster-based retrieval results presentation is based on the cluster hypothesis, which states that documents that cluster together have a similar relevance to a given query. However, while this hypothesis has been demonstrated to hold in classical information retrieval environments, it has never been fully tested in heterogeneous distributed information retrieval environments. Heterogeneous document representations, the presence of document duplicates, and disparate qualities of retrieval results, are major features of an heterogeneous distributed information retrieval environment that might disrupt the effectiveness of the cluster hypothesis. In this paper we report on an experimental investigation into the validity and effectiveness of the cluster hypothesis in highly heterogeneous distributed information retrieval environments. The results show that although clustering is affected by different retrieval results representations and quality, the cluster hypothesis still holds and that generating hierarchical clusters in highly heterogeneous distributed information retrieval environments is still a very effective way of presenting retrieval results to users.     

Using genetic algorithms to evolve a population of topical queries

Systems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engine interface. The quality of the material collected by such systems is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of this optimization problem are: (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, (3) the possibility of finding multiple solutions, and in many cases (4) the quest for novelty. This article describes optimization techniques based on Genetic Algorithms to evolve “good query terms” in the context of a given topic. The proposed techniques place emphasis on searching for novel material that is related to the search context. We discuss the use of a mutation pool to allow the generation of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction of queries containing novel but related terms.     

拟合用户偏好的个性化搜索

文章从用户偏好的角度对个性化搜索进行了优化研究,提出了基于语义关联树的查询扩展算法以及基于该算法的拟合用户偏好的个性化搜索系统架构。语义关联树可以灵活有效地控制查询扩展模型,在此之上的拟合用户偏好的个性化搜索系统具有用户偏好自学习能力。实验证明,该方法能有效提高文本检索的准确率。     

A case study of distributed information retrieval architectures to index one terabyte of text

The increasing number of documents to be indexed in many environments (Web, intranets, digital libraries) and the limitations of a single centralised index (lack of scalability, server overloading and failures), lead to the use of distributed information retrieval systems to efficiently search and locate the desired information. This work is a case study of different architectures for a distributed information retrieval system, in order to provide a guide to approximate the optimal architecture with a specific set of resources. We analyse the effectiveness of a distributed, replicated and clustered architecture simulating a variable number of workstations (from 1 up to 4096). A collection of approximately 94 million documents and 1 terabyte (TB) of text is used to test the performance of the different architectures. In a purely distributed information retrieval system, the brokers become the bottleneck due to the high number of local answer sets to be sorted. In a replicated system, the network is the bottleneck due to the high number of query servers and the continuous data interchange with the brokers. Finally, we demonstrate that a clustered system will outperform a replicated system if a high number of query servers is used, essentially due to the reduction of the network load. However a change in the distribution of the users’ queries could reduce the performance of a clustered system.     

基于多数据源的XQuery查询重写优化技术

本文主要阐述了XQuery对XML查询优化的相关内容,主要涉及的技术包括最小化、XML代数、索引技术、倒排列表、选择估计、重写机制等。本文阐述了其中基于多数据源的重写算法的具体思想和核心算法。