广域传感器数据库中多查询处理的研究

广域传感器数据库是目前国际上的一个热点研究领域.详细分析了广域传感器数据库中的查询处理技术,针对多个查询间存在的查询冗余问题,提出了一种多查询处理体系结构,并对体系结构中各模块的功能和实现算法进行了分析说明.理论分析和实验结果表明,此方法不仅可以有效地缩短用户访问的延迟时间,加快用户查询的速度,而且可以显著地减少传感器网络内部消息传递的数量,提高网络带宽的利用率.     

演绎数据库中的语义查询优化方法

语义查询优化的目的是使用语义知识来进行有效的查询，以提高查询效率，通过语义编译和语义转换，把一个查询转换成一个或多个更为有效的等价查询。本文介绍在演绎数据库中语义查询优化方法。     

广域传感器数据库中的缓存技术研究

广域传感器数据库是当前国际上备受关注的由多学科高度交叉的新兴热点研究领域。广域传感器数据库具有巨大的应用价值,应用前景十分广阔。基于缓存技术和预取技术,提出了一种缓存技术与预取技术相结合的体系结构。对体系结构中各个模块的功能和实现算法进行了详细阐述,对算法进行了复杂性和实例分析,有效地解决了广域传感器数据库系统中,低频结点数据进入缓存替换出高频结点数据所造成的缓存命中率低和系统资源浪费问题。     

谈数据库的查询优化

本文主要探讨了数据库查询优化的策略，其中包括基于索引的优化、基于SQL语句的优化和一些其它优化方法。     

无线传感器网络中的查询优化技术研究

对于无线传感器网络,用户需要通过查询网络中的感知数据来分析检测某个环境中的物理现象。和节点本地数据处理操作相比,数据通信消耗了网络的大部分能量。因此,如何优化查询以尽量减少数据通信量成为无线传感器网络中数据处理工作的核心技术之一。本文以两个典型的数据管理系统为例,探讨了传感器网络中查询优化的关键技术。     

无线传感器网络多查询的节能优化

提出了一个无线传感器网络多查询的节能优化方案。该方案通过建立相似查询判断算法把多查询中的相似查询分为一组,并在每一组找一个能使传输能耗达到最小的中继节点作为处理节点。组内节点的数据都传送到该处理节点,并由该节点利用数据处理函数处理数据,然后再传到基站。这样就减少了网络中数据的传输量,从而有效地节省了网络的能量,达到能量的最大化利用。     

无线传感器网络Top-k多查询算法

针对无线传感器网络中多个Top-k查询问题,提出了一种Top-k多查询处理的算法,对接收到的多个Top-k查询请求进行预处理,预处理依据是约束条件,得出两类不同的查询集合:单约束条件的多查询和多约束条件的多查询。针对单约束条件的多查询提出了ETOP算法,该算法首先对排在时间序列最前面的Top-k查询请求进行基于网内处理,然后把查询结果存入基站缓存,并把结果的最小值设定为阈值传输到各个节点,再根据后续查询请求的查询范围进行相应的查询,从而快速地获得Top-k查询结果。实验表明:Top-k多查询方法在能够很好地实现查询的同时,减少了无线传感器网络中的传输消耗和能量消耗。     

基于多数据库查询优化的图形工具的设计

设计了一个定义多库查询的工具,该工具的设计提供了一个新的抽象层面,该抽象层面向下可将查询映射到各种具体的数据库中,向上则隐藏了各个数据库的差异,在前端采用统一的方式显示给用户。用户可以以一种直观的方式定义查询,并且在查询执行过程中使用了查询采样优化算法,使得整个用户查询变得简单而快捷。     

数据库的查询优化研究

在以数据库为核心的应用系统中，查询操作最为频繁，实现快速查询将直接影响数据库应用系统的性能。正确地理解和恰当地使用索引可以在数据库中实现快速的数据查询。     

数据库查询优化的实现

目前所有的应用程序都要和数据库打交道,数据库的查询是应用程序的主要任务,查询的好坏也是衡量一个应用系统的主要参数。但是查询是要付出开销的,为此本文从实际就数据库的查询优化方法给予介绍,供大家参考。     

多重查询优化技术在移动数据库中的应用

移动计算的一个非常重要的特点就是在有限的网络带宽上进行数据分发．将多重查询优化技术应用于移动数据库的数据分发，中央服务器对某一时间段内移动客户的请求进行优化，然后通过一定的策略对数据集进行广播．模拟试验证明该数据分发策略较简单的基于拉动的策略极大地节约了网络带宽，缩短移动客户的平均等待时间．     

面向对象数据库查询优化策略在关系数据库中的应用

本文根据面向对象数据库对带有广义限制条件的查询语句的优化策略,比较了采用结构化查询语言表达此类查询的两种表述方式的执行效率,并推荐了基于反半连接的ＳＱＬ表述方式。     

Graph-Based Parallel Query Processing and Optimization Strategies for Object-Oriented Databases

Much work has been accomplished in the past on the subject of parallel query processing and optimization in parallel relational database systems; however, little work on the same subject has been done in parallel object-oriented database systems. Since the object-oriented view of a database and its processing are quite different from those of a relational system, it can be expected that techniques of parallel query processing and optimization for the latter can be different from the former. In this paper, we present a general framework for parallel object-oriented database systems and several implemented query processing and optimization strategies together with some performance evaluation results. In this work, multiwavefront algorithms are used in query processing to allow a higher degree of parallelism than the traditional tree-based query processing. Four optimization strategies, which are designed specifically for the multiwavefront algorithms and for the optimization of single as well as multiple queries, are introduced. The query processing algorithms and optimization strategies have been implemented on a parallel computer, nCUBE2; and the results of a performance evaluation are presented in this paper. The main emphases and the intended contributions of this paper are (1) data partitioning, query processing and optimization strategies suitable for parallel OODBMSs, (2) the implementation of the multiwavefront algorithms and optimization strategies, and (3) the performance evaluation results.     

Ranked Query Processing in Uncertain Databases

Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain” data, the data in the uncertain database are not exact points, which, instead, often reside within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) with monotonic preference functions over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic pruning, to help reduce the PRank search space. A special case of PRank with linear preference functions is also studied. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Furthermore, we propose and tackle the PRank query processing over the join of two distinct uncertain databases. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.     

非一致性数据库的概率查询重写

结合概率数据库技术,以元组匹配所产生的聚类为基础,提出了一种新的基于聚类的非一致性数据的概率方法。基于可信聚类,给出了基本的查询重写技术,在有聚集的查询中,考虑了合适的元组概率、区间值、期望值。在不进行程序预处理的情况下,“重写”能被商业数据库系统有效地优化和执行,采用不一致性数据的区分度和数据库大小去理解其适应性,并使用了TPC-H基准的数据和查询。实验显示了该方法的有效性。     

Optimal-Location-Selection Query Processing in Spatial Databases

This paper introduces and solves a novel type of spatial queries, namely, Optimal-Location-Selection (OLS) search, which has many applications in real life. Given a data object set D_A, a target object set D_B, a spatial region R, and a critical distance d_c in a multidimensional space, an OLS query retrieves those target objects in D_B that are outside R but have maximal optimality. Here, the optimality of a target object b in D_B located outside R is defined as the number of the data objects from D_A that are inside R and meanwhile have their distances to b not exceeding d_c. When there is a tie, the accumulated distance from the data objects to b serves as the tie breaker, and the one with smaller distance has the better optimality. In this paper, we present the optimality metric, formalize the OLS query, and propose several algorithms for processing OLS queries efficiently. A comprehensive experimental evaluation has been conducted using both real and synthetic data sets to demonstrate the efficiency and effectiveness of the proposed algorithms.     

Query Optimization for Deductive Databases

A systematic,efficient compilation method for query evaluation of Deductive Databases (DeDB) is proposed in this paper.In order to eliminate redundancy and to minimize the potentially relevant facts,which are two key issues to the efficiency of a DeDB,the compilation process is decomposed into two phases.The first is the pre-compilation phase,which is responsible for the minimization of the potentially relevant facts.The second,which we refer to as the general compilation phase,is responsible for the elimination of redundancy.The rule/goal graph devised by J.D.Ullman is appropriately extended and used as a uniform formalism.Two general algorithms corresponding to the two phases respectively are described intuitively and formally.     

基于云计算的多重查询优化系统

在常规海量数据分析作业中,CPU/IO密集型的查询语句通常复杂、耗时并存在大量可复用的公共部分。如何检测、共享和复用回归查询集中语句间的公共部分成为亟需解决的问题。为此,提出特征值索引方法,并构建适用于云计算场景的LSShare多重查询优化系统。基于查询语句的抽象语法树将语句划分为不同的查询层次,针对每个查询层次抽取特征向量并计算特征值。建立简单高效的特征值索引表以识别多重查询语句间的公共部分,并结合SQL重写技术来复用其中的公共部分。随着运行迭代次数的增加,LSShare系统将逐步优化云计算场景中的回归查询集。实验结果表明,该系统在运行效率上优于传统查询语句系统,可节约近1/3的执行时间。     

非一致性数据库的多关系聚集查询重写

基于非一致性关系数据库的选择连接查询技术,提出了基于非一致性数据库多个关系上的聚集查询重写方法.该聚集查询重写方法先通过查询出多关系上的一致性结果.然后进行分组聚集,返回聚集表达范围边界值.实验采用TPC-H决策支持基准进行性能研究,结果表明重写查询比初始查询的执行时间要长,但还是可以接受的,因此该方法是有效的.