Research on the Mass structured Data Storage and Sorting Algorithm and Methodology for SQL Database under the Big Data Environment

In this paper, we research on the research on the mass structured data storage and sorting algorithm and methodology for SQL database under the big data environment. With the data storage market development and centering on the server, the data will store model to data- centric data storage model. Storage is considered from the start, just keep a series of data, for the management system and storage device rarely consider the intrinsic value of the stored data. The prosperity of the Internet has changed the world data storage, and with the emergence of many new applications. Theoretically, the proposed algorithm has the ability of dealing with massive data and numerically, the algorithm could enhance the processing accuracy and speed which will be meaningful.

The Practice-oriented Platform of“Data Structure and Algorithm”Curriculum

"Data Structure and Algorithm",which is an important major subject in computer science,has a lot of problems in teaching activity.This paper introduces and analyzes the situation and problems in this course study.A "programming factory" method is then brought out which is indeed a practice-oriented platform of the teachingstudy process.Good results are obtained by this creative method.

Nearest neighbor search algorithm for GBD tree spatial data structure

This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.

Using Genetic Algorithm as Test Data Generator for Stored PL/SQL Program Units

PL/SQL is the most common language for ORACLE database application. It allows the developer to create stored program units (Procedures, Functions, and Packages) to improve software reusability and hide the complexity of the execution of a specific operation behind a name. Also, it acts as an interface between SQL database and DEVELOPER. Therefore, it is important to test these modules that consist of procedures and functions. In this paper, a new genetic algorithm (GA), as search technique, is used in order to find the required test data according to branch criteria to test stored PL/SQL program units. The experimental results show that this was not fully achieved, such that the test target in some branches is not reached and the coverage percentage is 98%. A problem rises when target branch is depending on data retrieved from tables;in this case, GA is not able to generate test cases for this branch.

A Multiple Model Approach to Modeling Based on LPF Algorithm

Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic idea is to find out, from vast historical system input-output data sets, some data sets matching with the current working point, then to develop a local model using Local Polynomial Fitting (LPF) algorithm. With the change of working points, multiple local models are built, which realize the exact modeling for the global system. By comparing to other methods, the simulation results show good performance for its simple, effective and reliable estimation.

Data Structures in Multi-Objective Evolutionary Algorithms

Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems （MOPs）. Multi-objective evolutionary algorithms （MOEAs） are considered an attractive approach for solving MOPs~ since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions （archives） may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Multiresolution analysis over triangle meshes: method and data structure

A robust and efficient algorithm is presented to build multiresolution models (MRMs) of arbitrary meshes without requirement of subdivision connectivity. To overcome the sampling difficulty of arbitrary meshes, edge contraction and vertex expansion are used as downsampling and upsampling methods. Our MRMs of a mesh are composed of a base mesh and a series of edge split operations, which are organized as a directed graph. Each split operation encodes two parts of information. One is the modification to the mesh, and the other is the dependency relation among splits. Such organization ensures the efficiency and robustness of our MRM algorithm. Examples demonstrate the functionality of our method.

An Improved HEAPSORT Algorithm with nlogn-0.788928n Comparisons in the Worst Case

A new variant of HEAPSORT is presented in this paper. The algorithm is not an internal sorting algorithm in the strong sense, since extra storage for n integers is necessary. The basic idea of the new algorithm is similar to the classical sorting algorithm HEAPSORT, but the algorithm rebuilds the heap in another way. The basic idea of the new algorithm is it uses only one comparison at each node. The new algorithm shift walks down a path in the heap until a leaf is reached. The request of placing the element in the root immediately to its destination is relaxed. The new algorithm requires about n log n - 0.788928n comparisons in the worst case and n log n - n comparisons on the average which is only about 0.4n more than necessary. It beats on average even the clever variants of QUICKSORT, if n is not very small. The difference between the worst case and the best case indicates that there is still room for improvement of the new algorithm by constructing heap more carefully.

Specification and Verification for Semi-Structured Data

Tree logic, inherited from ambient logic, is introduced as the formal foundation of related programming language and type systems, In this paper, we introduce recursion into such logic system, which can describe the tree data more dearly and concisely. By making a distinction between proposition and predicate, a concise semantics interpretation for our modal logic is given. We also develop a model checking algorithm for the logic without △ operator. The correctness of the algorithm is shown. Such work can be seen as the basis of the semi-structured data processing language and more flexible type system.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

基于遥感数据的工程结构损伤动态识别研究

以定期监测工程结构状况为目标,提出了基于遥感数据的工程结构损伤动态识别方法,解决获取影像粗糙问题,提升微小工程结构损伤识别效果。运用二维Tsallis交叉熵计算工程结构遥感影像的二维直方图,通过在布谷鸟搜索算法(CS)中引入Logistic映射的混沌扰动算子,形成混沌CS算法,完善二维Tsallis交叉熵的阈值选择过程,获取最佳阈值后,分割工程结构遥感影像,增强处理分割后的遥感影像,并将处理后的影像作为SVM分类识别模型输入,完成损伤动态识别、归类。实验结果表明,该方法获取遥感影像精度极高,分割影像清晰且各区域边缘完整,能够识别微小的裂缝损伤,最大程度还原裂缝宽度及线性特征,识别结果影像信息含量大,且识别均方误差低、平方相关系数高。

基于智能优化算法的高频变压器电磁结构优化设计

高频变压器(HFT)作为电力电子变换器等功率变换装备的核心部件,其优化设计是实现高功率密度、高效率和高可靠性的重要环节。为有效解决高频条件下显著的涡流效应和复杂紧凑的结构使变压器损耗难以准确计算、针对绝缘设计裕量不足的问题,本文提出计及高频效应和结构效应的电磁场建模方法,构建了高频变压器多目标协同优化设计方案。首先建立了低成本与高效率兼备的磁心损耗计算模型。其次,根据面积等效原理推导了考虑绕组结构效应的近似Dowell模型,实现绕组损耗的高精度计算。然后提出了考虑绕组端部效应和频率影响的漏感计算模型,减小漏感对于结构和频率的依赖性。在此基础上,采用一种新型多重绝缘结构,提高绕组间的绝缘耐压水平。最后,基于改进的非支配排序遗传算法(INSGA-Ⅱ)和自由参数扫描法建立了高频变压器的优化设计流程,根据筛选的最优设计方案研制了一台高频变压器样机。

混合式教学下学生计算思维能力培养——以燕山大学“数据结构与算法”课程改革为例

计算思维能力是软件工程专业学生的核心能力。混合式教学以其线上线下协同运作、资源先进、优势互补的特点,受到高校教师的关注。以“数据结构与算法”混合式教学改革为切入点,从线上MOOC资源、PTA上机实训和自主学习平台的构建、“任务/项目”为支架的授课形式、授课内容和作业设计、教师团队优化和教学评价设计等方面,搭建计算思维能力培养框架,以提升学生的知识水平、问题解决能力和算法思维能力,激发学生的好奇心和求知欲,提升计算思维水平。

城市轨道交通系统的层次化功能结构解析——以上海为例

解析城市轨道交通系统的功能结构对于建立以轨道交通为骨架的城市空间结构至关重要,其有助于优化城市空间布局,促进交通与土地利用融合,进而推动城市可持续发展。本文利用交通刷卡大数据,基于轨道交通站域的功能相似性和邻接关系提出了功能站组的概念,并形成了一套“站域功能分类—站组范围划定—站群结构识别”的方法体系。以上海市轨道交通系统为例,针对单个站域,构建表征站域土地利用功能的连续客流时间序列,并依据时间序列特征分类得到站域功能类型;将多个具有相似的出行模式和土地利用功能的相邻站域划定为功能站组;以功能站组为基本单元,采用社区发现算法,对功能站组间的客流交互网络进行分析以识别站群。研究结果表明,城市轨道交通系统的“站域—站组—站群”层次化功能结构解析方法综合了场所空间和流空间视角,有助于认识特大城市轨道交通系统的功能结构特征,并为轨道交通系统的发展提供多层次的空间优化建议。

图神经网络研究综述

随着人工智能的快速发展,深度学习已经在图像、文本和语音等可在欧氏空间表示的数据中取得了巨大成功,但却一直无法很好地应用于非欧氏空间。近年来,图神经网络在非欧几里得空间中展现出了强大的表示学习能力,并广泛应用于推荐系统、自然语言处理以及机器视觉等众多领域。图神经网络模型基于信息的传播机制,具体地,图中的目标节点通过聚合邻居节点的信息来更新自身的嵌入表示。利用图神经网络,可将众多现实问题(如社交网络、知识图谱和药物化学成分等)抽象成图网络,借助图中的连接边,对不同节点之间的依赖关系进行合理建模。鉴于此,对图神经网络进行了系统综述,首先介绍了图结构数据方面的基础知识,然后对图游走算法和不同类型的图神经网络模型进行了系统梳理。进一步地,详细阐述了当前图神经网络的通用框架和应用领域,最后对图神经网络的未来进行了总结与展望。

XPSort——树形数据多核并行外存排序算法

XML数据处理中一个基本问题是树形数据排序.本文针对已有算法的不足提出了一种XML文档多核并行外存排序算法——XPSort.XPSort扫描XML文档产生相互独立的排序任务,利用多核CPU对任务进行并行处理;同时,利用数据压缩、单临时文件以及避免子树匹配等策略,有效地减少磁盘I/O,提高排序性能;它克服了NEXSORT算法没能有效利用内存空间、存在大量随机I/O的问题以及难以处理"右深树"的缺陷,也克服了HERMES的数据冗余、大量磁盘开销等缺点.文章对不同特性的XML文档开展了大量比较实验,结果表明XPSort优于已有算法,所提优化方法是有效可行的.

基于非支配遗传算法的HLA仿真系统数据采集策略

数据采集是仿真执行过程中的重要环节,数据采集的完整性和效率对整个训练仿真活动的最终效果和效率具有重大影响。然而,在现有基于高层体系结构(high level architecture, HLA)的分布式仿真系统中,集中式数据采集在单个步长内读写海量数据,会影响仿真正常推进,而分布式数据采集会造成大量冗余数据,且采集模块的开发不具备通适性。针对上述问题,基于弱分布式数据采集结构,利用多个采集成员实现并行数据采集,并基于非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithm Ⅱ, NSGA-Ⅱ)制定采集任务在多个成员间的分配策略,实现数据采集负载的均衡分布。仿真结果和真实系统上的实验结果表明,所提方法能显著提升数据采集效率,同时减少数据采集成员执行过程中的中央处理器(central processing unit, CPU)和内存消耗。

基于隐结构模型和频繁项集的针刺治疗慢性前列腺炎辨证取穴规律

目的:探讨针刺治疗慢性前列腺炎的辨证取穴规律,为临床治疗慢性前列腺炎提供帮助。方法:检索国家知识基础设施数据库(CNKI)、中文科技期刊数据库(CCD)等数据库中关于针刺辨证论治慢性前列腺炎的文献,构建包含症状、证型、穴位处方的慢性前列腺炎病历数据库,运用隐结构模型分析、频繁项集等方法,分析针刺治疗慢性前列腺炎的辨证取穴规律。结果:共纳入文献64篇,涉及穴位91个,症状248项。高频穴位如三阴交、中极等;高频症状包括舌体瘀点瘀斑、苔黄腻、滴白、尿急等;构建隐结构模型,得出慢性前列腺炎主要证型有湿热下注、肾阳不足等;挖掘出症状-穴位频繁项集、症状-证型-穴位频繁项集各4项。症状-穴位频繁项集如“尿急+滴白+阳痿+早泄+肾俞+足三里”,症状-证型-穴位频繁项集如“尿频+尿急+苔黄腻+滴白+舌体瘀点瘀斑+湿热瘀阻+三阴交+会阴”,提示治疗时可根据相应症状判定证型及选择对应穴位。结论:针刺治疗慢性前列腺炎多以三阴交、中极、关元等为主要穴位,穴位配伍依据临床情况辨证选穴,此可为临床治疗慢性前列腺炎提供参考。

动态内全潜结构投影的空间扩展故障检测方法

动态内偏最小二乘(DiPLS)方法是基于数据驱动的潜结构投影的动态扩展算法,用于动态特征提取和关键性能指标预测.在大型装备系统中,传感器采集的当前时刻样本受历史样本的影响且可能包含较大噪声.在动态特征提取中,因DiPLS算法未按降序提取主成分,导致残差空间仍存在较大变异,动态和静态信息难以有效分离,影响故障检测性能.为此,本文提出了一种基于动态内全潜结构投影的故障检测方法(DiTPLS).首先,使用动态内偏最小二乘方法和向量自回归模型建立动态模型并检测故障,用于捕捉质量相关动态信息;基于结构化动态主成分分析算法建立一种改进的动态潜在变量模型,用于残差分解,提取质量无关的动态信息和静态信息,并构造合适的统计量进行故障检测.数值仿真和田纳西–伊斯曼过程实验验证了DiTPLS算法的有效性.