基于成本的数据库查询优化浅析

主要介绍基于成本的数据库查询优化的一些基本概念,针对多表连接的三种方法：嵌套循环链接、归并连接和混合连接进行分析和阐述．并成本估算,列出估算公式,     

面向移动计算环境的单连接查询处理模式研究

在移动计算环境下，基于准确的操作代价估算结果来选择合适的连接查询处理模式，可以减少数据的传输量和移动设备的能量消耗。探讨了该环境下移动设备能量消耗的一个新的非对称特征，提出了一种操作代价估算方法，并从数据传输量和能量消耗两个方面对连接查询处理模式进行了代价估算和性能比较，提出了4个实用准则，以指导连接查询处理模式的选择。试验结果充分论证了估算方法和准则的正确性，且比现有同类估算模型和结论具有更加广泛的应用范围。     

蛇型弹簧联轴器的结构优化及应用

带式输送机是散杂货码头货物运输的关键设备之一,其驱动部的稳定运行和维护便捷度对货物运输效率起决定性作用。本文通过设计计算和应力分析,结合带式输送机的实际使用情况和现场安装要求,在不改变其他设备的前提下,采用胀套连接和键槽连接相结合的连接方式,有效提高驱动部整体运行稳定性,降低设备维护成本,延长设备使用寿命。     

半连接查询优化算法的研究

为了提高分布式数据库管理系统的查询效率,分析了分布式数据库管理系统的特点,找出了影响分布式数据库管理系统查询效率的关键因素,讨论了直接连接查询的常见策略和半连接查询的原理、实现方法以及所花费的传输代价,最后结合分布式数据库管理系统的具体实例提出了一种半连接查询策略。改进后的半连接查询策略优化了连接方案,降低了数据传输过程的成本,缩短了查询处理的响应时间,提高了查询操作的效率。     

应对倾斜数据流在线连接方法

并行环境下的分布式连接处理要求制定划分策略以减少状态迁移和通信开销。相对于数据库管理系统而言,分布式数据流管理系统中的在线θ连接操作需要更高的计算成本和内存资源。基于完全二部图的连接模型可支持分布式数据流的连接操作。因为连接操作的每个关系仅存放于二部图模型的一侧处理单元,无需复制数据,且处理单元相互独立,因此该模型具有内存高效、易伸缩和可扩展等特性。然而,由于数据流速的不稳定性和属性值分布的不均衡性,导致倾斜数据流的连接操作易出现集群负载不均衡的现象。针对倾斜数据流的连接操作,模型无法动态分配查询节点,并需要人工干预数据分组的参数设置。尤其是应对全部历史数据的连接查询,模型效率更低。基于上述问题,提出了管理倾斜数据流连接的框架,使用基于键值和元组混合的划分样式有效应对二部图模型的各侧倾斜数据。并设计了重新动态分配查询节点的策略和状态迁移算法,以支持全历史数据的连接查询和自适应的资源管理。针对合成数据和真实数据的实验表明,该方案可有效应对倾斜数据的连接操作并进一步提升分布式数据流管理系统的吞吐率,特别是降低云环境中的计算成本。     

云计算环境下有线通信网络连接方案优化

随着信息技术的不断发展,智能化网络在人们生活中得到了普及,为有线通信网络连接方案优化提供了基础条件,确保有线通信实现稳定连接。基于此,总结了有线通信网络的发展现状,并从云计算处理环境的建设、设计有效的连接方案、控制效率成本和连接方案的整体性优化理论四个方面,论述了云计算环境下有线通信网络连接方案的优化方法。     

基于生物脑网络连接特性的高效神经网络算法

人工神经网络发展至今,已经在计算机视觉、类脑智能等方面得到广泛应用.在过去几十年中,人们对神经网络的研究注重追求更高的准确率,从而忽略了对网络计算成本的控制.而人脑作为高效且节能的网络,其对人工智能的发展起到了重要启示作用.如何仿真生物脑网络的连接特性,建立超低能耗的人工神经网络模型实现基本相同的目标识别正确率成为当前研究的热点.为建立低能耗的人工神经网络模型,本文结合大脑网络的连接特性,通过改变人工神经网络的连接实现网络的高效性.实验结果表明,结合生物脑网络的连接特性,改变网络的连接,很大程度上减少了网络的计算成本,而网络的性能并没有受到明显影响.     

ZigBee技术应用物联网行业中的前景

正最近有相当多的讨论是关于物联网和不久即将部署的数十亿可连接设备。这些设备的大多数不是智能手机或其他通讯设备,而是那些可以使我们的家庭、工厂、汽车和其他更多系统被无线连接的遥控器和传感器,并且允许他们在远程处理器或手动控制下更有效的运作。为了满足这种无处不在的物联网连接需求,就必须有一个满足可连接设备需求的网络标准。蜂窝技术太复杂,使用它将会大大增加成本,并且不支持这些大多数设备所需的电池寿命要求。     

关系数据库中多表连接方式应用研究

以学生入学报到信息管理系统为例,对关系数据库中表的外连接、内连接,以及交叉连接进行了案例分析和应用,以区分不同连接方式的异同和各自适用的场合.     

P2P网络中远程连接构建方式对查询的影响

为了提高网络的查询速度,降低查询成本,近年来小世界理论被引入P2P网络,并通过构建远程连接提高网络的查询性能.本文主要研究了P2P网络中远程连接的构建问题,并根据实际网络中查询对象的分布,提出了一种基于历史信息构建远程连接的方式.本文通过仿真实验分析比较了三种远程连接的构建方式,结果显示基于历史信息选择远程连接的方式,可以在实际P2P网络具有更短的平均查询路径长度,更低的构建和维护成本以及良好的鲁棒性.     

Ed-Sjoin:一种优化的字符串相似连接算法

相似连接(similarity join)在数据清洗、生物信息、模式识别等应用领域中有着广泛应用,其中基于编辑距离的字符串相似连接是一种重要的相似连接.尽管当前有一些基于编辑距离的字符串连接算法提出,然而,当前的算法存在着大量的多余计算,影响了算法的效率.为了高效计算基于编辑距离的字符串连接,提出了一种优化的算法Ed-sjoin,分别从优化筛选算法和基于前缀的重复消减策略两方面对算法进行优化,这些优化策略可以实现更加有效的剪枝,并且避免了部分重复计算,从而加速算法的执行.实验结果表明,提出的方法优于现有方法.     

Fast similarity join for multi-dimensional data

The efficient processing of multidimensional similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focused on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memory available on current computers, and the need for efficient processing of spatial joins suggest that spatial joins for a large class of problems can be processed in main memory. In this paper, we develop two new in-memory spatial join algorithms, the Grid-join and EGO*-join, and study their performance. Through evaluation, we explore the domain of applicability of each approach and provide recommendations for the choice of a join algorithm depending upon the dimensionality of the data as well as the expected selectivity of the join. We show that the two new proposed join techniques substantially outperform the state-of-the-art join algorithm, the EGO-join.     

基于分片的XML快速结构连接算法

结构连接作为XML查询的重要部分,对查询性能来说起着非常重要的作用.目前有几种结构连接算法已经被提出,例如Stack-Tree、XR-tree.这些算法主要集中在节点之间关系的确定上.与之不同,作者从分片的角度去解决结构连接问题,首先把节点间的关系引申到分片之间的关系,从而得出各分片之间的一些性质,再利用分片间的性质来提高结构连接操作的性能.文中提出了一种基于分片的结构连接算法和两种优化方法,实验表明该算法在性能上要优于Stack-Tree算法和XR-tree算法.设计了一个简单而又高效的索引结构来存储分片结果,实验结果表明该索引结构的维护代价要小于XR-tree的维护代价.     

位图连接索引服务机制研究

位图连接索引是数据仓库中一种有效的优化表间连接操作性能的索引机制。在大内存分析处理应用场景下,位图连接索引不仅需要权衡索引的内存和CPU开销,还需要进一步考虑处理器平台所带来的性能收益和数据访问延迟。提出了基于服务的位图连接索引管理机制,其主要特点体现在三个方面：独立于数据库的自管理索引机制;基于存储空间约束的TOP K关键字位图连接索引机制;处理器敏感（processor-conscious）的位图连接索引技术。索引服务将索引从数据库中内置的数据结构变成数据库外的索引服务层,通过对用户查询负载的分析模块和索引服务管理模块改变传统的由数据库管理员人工管理索引的模式,同时借助于协处理器和内存云技术提高索引服务的性能和灵活性。实验测试结果表明,索引服务机制能够有效地提高索引存储和访问效率,在通用GPU的强大并行处理能力的支持下,位图连接索引服务的性能和数据库整体查询处理性能都得到了显著的提升。     

Optimal parallel scheduling of M-way join queries

The problem of computing multirelation (M-way) join queries on uniprocessor architectures has been considered by many researchers in the past. This paper lays the necessary foundation for work involving optimization of M-way joins in parallel architectures. We explain the inadequacies of previous uniprocessor strategies and describe a more suitable formulation based on the concept of matching in graph theory to approach the problem in a parallel environment. It has been shown that the problem of optimizing M-way joins is an NP-hard problem and hence we would expect that in a parallel processing environment the search space of possible solutions (join schedules) would be enormous, especially when a variable number of processors are considered. Our strategy seeks to reduce the region to search by partitioning the search space according to the number of available processors. Based on this a significant portion of the search space, which will produce non-optimal join schedules, may be ignored.     

Structural Join and Staircase Join Algorithms of Sibling Relationship

The processing of XML queries can result in evaluation of various structural relationships. Efficient algorithms for evaluating ancestor-descendant and parent-child relationships have been proposed. Whereas the problems of evaluating preceding-sibling-following-sibling and preceding-following relationships are still open. In this paper, we studied the structural join and staircase join for sibling relationship. First, the idea of how to filter out and minimize unnecessary reads of elements using parent＇s structural information is introduced, which can be used to accelerate structural joins of parent-child and preceding-sibling-following-sibling relationships. Second, two efficient structural join algorithms of sibling relationship are proposed. These algorithms lead to optimal join performance： nodes that do not participate in the join can be judged beforehand and then skipped using B^＋-tree index. Besides, each element list joined is scanned sequentially once at most. Furthermore, output of join results is sorted in document order. We also discussed the staircase join algorithm for sibling axes. Studies show that, staircase join for sibling axes is close to the structural join for sibling axes and shares the same characteristic of high efficiency. Our experimental results not only demonstrate the effectiveness of our optimizing techniques for sibling axes, but also validate the efficiency of our algorithms. As far as we know, this is the first work addressing this problem specially.     

Join operations in temporal databases

Joins are arguably the most important relational operators. Poor implementations are tantamount to computing the Cartesian product of the input relations. In a temporal database, the problem is more acute for two reasons. First, conventional techniques are designed for the evaluation of joins with equality predicates rather than the inequality predicates prevalent in valid-time queries. Second, the presence of temporally varying data dramatically increases the size of a database. These factors indicate that specialized techniques are needed to efficiently evaluate temporal joins.We address this need for efficient join evaluation in temporal databases. Our purpose is twofold. We first survey all previously proposed temporal join operators. While many temporal join operators have been defined in previous work, this work has been done largely in isolation from competing proposals, with little, if any, comparison of the various operators. We then address evaluation algorithms, comparing the applicability of various algorithms to the temporal join operators and describing a performance study involving algorithms for one important operator, the temporal equijoin. Our focus, with respect to implementation, is on non-index-based join algorithms. Such algorithms do not rely on auxiliary access paths but may exploit sort orderings to achieve efficiency.Received: 17 October 2002, Accepted: 26 July 2003, Published online: 28 October 2003Edited by: T. Sellis     

Data Partitioning for Parallel Spatial Join Processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. In this paper we show that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.