一种基于SQL变换的分布式空间查询优化算法

基于新一代GIS技术体系的空间数据的分布特点,提出了一种新的应用于分布式空间查询处理的混合启发式优化算法(HHOA).该算法参考了传统的基于关系代数变换的启发式优化算法,同时为了克服单调采用直接连接或者半连接的弊端,引入了直接连接和半连接相混合的策略,实现更高的查询执行效率.实例研究表明,尤其是在涉及空间连接的查询前提下,该算法能够有效支持分布式空间查询的处理.     

分布式数据库系统的查询优化策略研究

在分析比较分布式数据库系统和集中式数据库系统查询优化目标不同特点的基础上，归纳出分布式数据库系统的查询优化目标，进而提出查询优化的策略，并在举例中重点讨论了操作执行顺序的不同对查询性能的影响．     

分布式多数据库查询平台TCMMDB的关键技术的实现

 主要讨论了一个分布式多数据库查询平台ＴＣＭＭＤＢ的实现技术,首先,介绍了ＴＣＭＭＤＢ的整体系结构;然后分别介绍了ＴＣＭＭＤＢ的两个主要部件：全局管理和局部代理的具体实现技术,并在其中着重介绍了查询优化、同步控制和错误处理等关键技术。     

基于分布式异构数据库的查询优化算法

对于分布式异构数据库,查询优化既是非常复杂的问题,又是影响系统性能的关键因素。结合遗传算法和模拟退火算法的优点,提出基于遗传模拟退火算法的分布式异构数据库查询优化方法。仿真实验表明,该方法有效地提高分布式异构数据库的查询优化效率。     

WPERF+:一种有效的分布式查询处理优化算法

分布式查询是一种网络通信成本较大的查询过程,设计和开发出有效的查询策略和技术是分布式查询面临的挑战。本文展示了一种新的分布式查询的算法。即WPERF 连接算法,它结合了W算法和PERF算法的特点。进一步强化了W算法和PERF的性能。文章从理论上证明了WPERF 算法具有有效降低分布式查询处理成本的效能。     

基于Oracle9i数据库的查询优化

在企业信息化进程中,数据库查询是不可或缺的重要环节,数据库技术是一门新兴技术,但隶属于第三次工业革命——信息技术。如何实现快速、安全、优质、高效查询所需要的数据,就需要对数据库查询进行必要的优化。文章以Oracle9i数据库为例,系统的阐述了对Oracle9i数据库查询顺序、查询优化、查询对象等方面的数据库优化问题,希望能够为从事这个行业相关人员提供一些参考。     

分布式数据库查询优化算法研究

查询优化是数据库研究的一个焦点,尽管有很多研究人员做了很多的工作,但是与关系数据库技术应用数据处理过程的成功相比,多连接查询优化仍然是在关系数据库系统中没有得到很好的解决。分布式数据库的查询处理和优化是影响其系统性能、服务效率和可靠性的关键因素。     

Web的财务查询系统的性能优化与实现

在数据库和应用程序设计等方面探讨了信息查询的性能问题,研究了基于Web方式数据查询的优化方法.在高校财务信息查询系统的设计中,通过物理数据库的优化设计、使用优化的SQL语句、使用存储过程和基于存储过程的分页显示技术等优化信息查询性能,并给出了具体的代码实现,从而在软件方面解决了信息查询系统的性能问题.     

GML即时查询引擎研究与实现

针对面向内存结构的地理标记语言(GML)查询方法在处理大数据量时耗费大量计算资源,而采用过滤器和索引等技术来解决又会使数据处理过程复杂化的问题,研究了GML即时查询引擎的设计与实现.首先设计了一种接口灵活的GML查询语言GeoXPath,在深入分析相关引擎实现机制的基础上,提出了一种基于非抽取解析方式和具有简洁数据结构的GML即时查询引擎,该引擎采用GML结构直接转换等优化方法,无需附加流程.实验表明,所实现的引擎具有很好可用性,既可用于普通计算环境下较大GML文档的即时查询,也可结合过滤器和索引技术进一步提高GML空间数据库的查询效率.     

基于Solr的海量日志信息查询性能优化的研究

ApacheSolr是一个基于ApacheLucene的开源企业搜索服务器。Apache SolrCloud是基于Solr和Zookeeper的分布式搜索方案,它的主要思想是使用Zookeeper作为集群的配置信息中心。文章在海量日志索引信息存储和查询方面进行了探索,证明了在相关优化策略下Solr Cloud具备了优秀的查询性能。     

Organizing distributed data bases for parallel searching

Abstract

This paper is mainly concerned with the problem of distributing a data base (i.e., a set of segments) in a computer network system so as to facilitate parallel searching. In our distributed data base model, we assume that all segments are stored in nodes. Each time a query occurs, all nodes are searched concurrently. For convenience, we define the time required to access a segment from any node as a time unit. For a network with d nodes, the response time of a query is then identical to the maximum (n ₁ , n ₂, …, n_d ), where n_i , is the number of segments that satisfies the query and is stored in node i. Unfortunately, the solution for finding an optimal way to organize a distributed data base for parallel searching is still at large. In other words, given a data base, there is no efficient polynomial time algorithm for finding an optimal arrangement of segments onto nodes. In this article, we shall present a “heuristic algorithm” based upon a multivariant analysis method in statistics to distribute a data base in a network system. Some experimental results will show that our method is indeed feasible and effective.     

Coloured Petri net‐based modeling for distributed relational database queries

Abstract

In this paper, a new application of colour timed Petri net (CTPN) based methodology for distributed relational database queries modeling and corresponding simulation is addressed. This work, first based on parsed query definitions, converts logical query plans into designed CTPN models. Later, the improved cost‐based functions are then established and appended to CTPN with a general purpose CPN simulator‐Design/CPN to observe the thresholds of query operations. The propose CTPN can be used in the design phase as an experimental prototype to automatically simulate distributed database query processing which, in turn, may considerably reduce the load of developing the actual query processing software in the logical design phase of a distributed database system. Also, since all the essential details of query processing in CTPN have been simulated, the results of this study can be closely related to real world applications.     

分布式包装实时数据库ARS算法应用

目的研究具有连续状态空间的复杂包装产品信息在分布式网络实时数据库中的查询方式。方法通过结合增强学习(EL)和自适应共振结构神经网络(ARS),给出一种基于增强学习的自适应共振结构神经网络算法——ELARS2。在ARS2算法中引入增强学习的选择和评估方式,解决在ARS2算法中分类模式的查询问题。设计在存储空间中使用分布式网络实时数据库查询目标的仿真试验,并用2种ELARS2算法(TDARS2和QARS2算法)来实现,并与经典的EL算法进行对比。结果 2种ELARS2算法完成查询目标的平均时间显著小于经典的EL算法。结论在2种ELARS2算法中,TDARS2比QARS2效果更好。     

DHT系统的安全性优化方法研究

对分布式哈希表(DHT)系统的安全脆弱性问题进行了研究,提出了多种安全性优化策略,并给出了一个原型系统。进行了真实网络实验,实验数据表明,现有DHT网络易受索引毒害和路由污染攻击,产生的错误查询结果甚至会引发更大规模的网络安全事件。通过改进一个个DHT系统的节点ID生成机制、路由表更新机制和搜索路径选择机制,从系统运行的各个阶段提升其安全场,抵御攻击者共谋。基于上述方法设计的原型系统在保证平均查询跳数增加不到1跳的情况下,在共谋攻击节点占比60%的网络中,将系统查询成功率保持在65%以上,其方法适用于各种分布式哈希表结构,具有重要的实际应用前景。     

Finite elements for an optimal control problem governed by a parabolic equation

A stationary variational formulation of the necessary conditions for optimality is derived for an optimal control problem governed by a parabolic equation and mixed boundary conditions. Then a mixed finite element model with elements in space and time is utilized to solve a simple numerical example whose analytical and finite difference solutions are given elsewhere. Numerical results show that the proposed method with C^° continuity elements constitutes a powerful numerical technique for solution of optimal control problems of distributed parameter systems.     

Optimal feeding profile in fed-batch bioreactors using a genetic algorithm

An optimal feeding profile for a fed-batch process was designed based on an evolutionary algorithm. Usually the presence of multiple objectives in a problem leads to a set of optimal solutions, commonly known as Pareto-optimal solutions. Evolutionary algorithms are well suited for deriving multi-objective optimisation since they evolve a set of non-dominated solutions distributed along the Pareto front. Several evolutionary multi-objective optimisation algorithms have been developed, among which the Non-dominated Sorting Genetic Algorithm NSGA-II is recognised to be very effective in overcoming a variety of problems. To demonstrate the applicability of this technique, an optimal control problem from the literature was solved using several methods considering the single-objective dynamic optimisation problem.     

Multi-dimensional Security Range Query for Industrial IoT

The Internet of Things (IoT) has allowed for significant advancements in applications not only in the home, business, and environment, but also in factory automation. Industrial Internet of Things (IIoT) brings all of the benefits of the IoT to industrial contexts, allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy. The expansion of the IoT has been set by serious security threats and obstacles, and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control. A privacy-preserving multi-dimensional secure query technique for fog-enhanced IIoT was proposed in light of the fact that most existing range query schemes for fog-enhanced IoT cannot provide both multi-dimensional query and privacy protection. The query matrix was then decomposed using auxiliary vectors, and the auxiliary vector was then processed using BGN homomorphic encryption to create a query trapdoor. Finally, the query trapdoor may be matched to its sensor data using the homomorphic computation used by an IoT device terminal. With the application of particular auxiliary vectors, the spatial complexity might be efficiently decreased. The homomorphic encryption property might ensure the security of sensor data and safeguard the privacy of the user's inquiry mode. The results of the experiments reveal that the computing and communication expenses are modest.     

Optimal design of distributed treatment systems for the effluents discharged to the rivers

This paper presents a methodology for the optimal synthesis of distributed treatment systems of effluents discharged into a main river to meet water discharge quality constraints. The methodology is based on a new superstructure that is formulated and solved as a multi-objective mixed-integer nonlinear programming model. A material flow analysis technique is used to track the pollutants through the watershed considering the combined effects of the inputs, outputs (i.e., agricultural, residential, industrial, and so on) and the chemical transformations. A disjunctive programming model is implemented for selecting the optimal location of the distributed treatment system. Prior to the optimization and based on the pollutants considered, a discretization approach is implemented to determine from simulation the removal efficiency and the unit cost of given configurations and operating conditions of the selected treatment units. Therefore, the optimization process determines the removal efficiency used to treat the effluents and the flow rate treated. Simultaneous minimization of the total annual cost of the distributed treatment system and the contaminant concentration of the discharge to the catchment of the watershed are considered as two objective functions. Three case studies (one in Mexico and two in Egypt) have been selected to illustrate the methodology. Results show that significant savings can be obtained when the distributed treatment system is implemented. Finally, the proposed methodology can be used for supporting governmental decisions (i.e., it provides the investment required for a specific water quality).