Construction of Mediators for Heterogeneous Data Source Integration Systems

To construct mediators for data integration systems that integrate structured and semi-structured data, and to facilitate the reformulation and decomposition of the query, the presented system uses the XML processing language (XPL) for the mediator. With XPL, it is easy to construct mediators for data integration based on XML, and it can accelerate the work in the mediator.

A Dynamic XML-NS View Based Approach for the Extensible Integration of Web Data Sources

We propose a three-step technique to achieve this purpose. First, we utilize a collection of XML namespaces organized into hierarchical structure as a medium for expressing data semantics. Second, we define the format of resource descriptor for the information source discovery scheme so that we can dynamically register and/or deregister the Web data sources on the fly. Third, we employ an inverted-index mechanism to identify the subset of information sources that are relevant to a particular user query. We describe the design, architecture, and implementation of our approach—IWDS, and illustrate its use through case examples. Key words integration - heterogeneity - Web data source - XML namespace CLC number TP 311.13 Foundation item: Supported by the National Key Technologies R&D Program of China(2002BA103A04)Biography: WU Wei (1975-), male, Ph.D candidate, research direction: information integration, distribute computing

Overview of global monthly surface temperature data in the past century and preliminary integration

This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Software Metric Analysis of Open-Source Business Software

Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.

Benchmarking of JEFF-3.2, FENDL-3.0 and TENDL-2014 evaluated data for tungsten with 14.8 MeV neutrons

Integral experiments on tungsten slab samples were carried out on the D-T neutron source facility at China Institute of Atomic Energy. Leakage neutron spectra from the irradiated tungsten target were measured by the time-of-flight technique. Accuracy of the nuclear data for tungsten was examined by comparing the measured neutron spectra with the leakage neutron spectra simulated using the MCNP-4C code with evaluated nuclear data of the JEFF-3.2, FENDL-3.0 and TENDL-2014 libraries. The results show that the calculations with JEFF-3.2 agree well with the measurements in the whole energy range and all angles, whereas the spectra calculated with FENDL-3.0 and TENDL-2014 have some discrepancies with the experimental data.

空地一体遥感技术在轨道交通安全保护区巡查中的应用

随着城市轨道交通建设的不断发展,轨道交通沿线安全保护区巡查工作的范围越来越大,内容越来越多,区域内的情况越来越复杂。针对存在的问题,本文构建了一套空地一体的遥感综合监测体系,在巡查工作中引入航空摄影测量、低空无人机监测、车载全景扫描、定点实时监控等多种遥感监测技术及DSM差分计算、深度学习智能识别等多种数据处理技术,并以上海为例开展应用实践。结果表明,本文方法具有可行性,对建立全覆盖、高频次、智能化的巡查模式有参考借鉴价值。

基于BP神经网络的伪设计样本融合研究

由于实施简单、样本收集快速,非概率抽样已经成为获取数据的重要方式之一,但非概率样本代表性往往无法得到验证,样本无法多次利用,从而造成了数据资源浪费的问题。融合概率样本及非概率样本进行统计推断可以进一步提升估计的精度,减少数据资源的浪费。由此,本文采用伪设计样本融合思路,提出基于BP神经网络的伪设计样本融合方法,创新性地将BP神经网络引入非概率样本权重构造中,并采用最短距离法对倾向得分生成的初始权重进行二次校准。实验结果显示,本文提出的方法可以获得更高精度的估计结果,在协变量维数较高的情况下仍能保持良好的估计精度。本文不仅为网络时代下海量的高维样本融合分析提供了一个切实的方法,并且为概率样本与非概率样本融合领域的研究提供了一种新的思路。

基于随机森林的多源小样本数据快速集成方法

受多源小样本数据属性复杂性的影响,对其进行集成处理时,过拟合和欠拟合情况较为明显。为此,文章提出基于随机森林的多源小样本数据快速集成方法。考虑多源小样本数据自身的属性特征,在构建随机森林模型阶段,充分利用粒向量与多源小样本数据特征的贴合性,将其作为随机森林的基础结构,利用粒化层归一化多源小样本数据,并将输出的粒化结果作为决策层的节点。在集成阶段,根据多源小样本数据与决策层节点之间的距离,集成数据。在测试结果中,数据集成的过拟合情况占比仅为0.29%,欠拟合情况占比也仅为0.27%,具有良好的集成效果。

基于气象要素驱动的流域水旱灾害智慧“四预”系统研究及应用探析

为助力数字孪生水利体系建设,以探索流域水文与气象的复杂联动关系为基础,研究长期气候变化和短期极端天气事件对流域水旱灾害发生频率的潜在影响,推动关键气象要素驱动数据集的多源数据融合和水文气象预测平台的构建,通过中短期极端气象事件的实时预报和同步解译,提升流域防洪抗旱决策制定及工作部署的能力和反应速度。研究成果对于推动数字孪生流域水旱灾害“四预”精细化和智慧化具有重要意义。

山东黄河水文数字孪生关键技术研究及应用

为实现黄河流域防汛“四预”功能,对河道洪水、漫滩演进等过程进行精准的动态化预测预报,构建山东黄河水文数字孪生示范平台,介绍数字孪生构建过程中的多源异构数据融合、水动力学模型构建等关键技术,重点阐述基于流体解算的漫滩仿真和仿真推演方法、三维模拟和动态可视化渲染、洪水演进推演可视化配置等,可动态展示漫滩河段槽蓄量、滩蓄量、淹没趋势等核心数据,自主生成区域洪水灾害影响仿真评估报告。关键技术主要应用于洪水灾害防御业务、水资源优化配置及管理、水利工程全周期管理、河湖保护治理等领域,服务黄河防汛抗旱指挥、河道各级主管等部门,助力新阶段水利高质量发展。

金山湖流域三水协同调控决策支持平台

流域综合治理/三水协同调控是净化水体水质、消除水体黑臭、恢复水生生态系统健康/优化水资源调控、改善水环境质量、修复水生态系统的必要途径,快速高效精准的决策支持平台是实现流域综合治理/三水协同调控优化决策制定的重要工具/关键抓手。针对金山湖流域面临的多水共治和水环境水生态安全保障等难题,为实现金山湖流域三水融合协同共治及其靶向精准高效决策,基于“空—地—水”一体化模型体系与流域智慧化管理平台技术成果,提出了决策支持平台体系结构。部署具有相对独立功能的数据中心、计算中心、控制中心以及业务中心,通过数据融合集成、模型集成等关键技术,构建了金山湖流域三水协同调控决策支持平台。平台实现了气象、水文和水质等多源异构数据的采集传输、存储管理及交换共享,集成了包括气象、陆地和水体模块的多过程耦合模型的按需调用、智能驱动以及水资源保障、水环境提升、水生态修复等多目标协同的调控治理方案设置、过程模拟、效果评估和最优决策等功能。为流域污染负荷控制—水体水力提升—生态功能恢复等多目标统筹、靶向治理的调控方案制定提供精准高效决策等技术服务,提高了流域水量水质水生态协同调控的决策能力与效率,支撑金山湖流域三水量、质、效协同发展。

智能化多源多维大数据融合设计在卫生应急中的应用

目的/意义针对来源复杂、标准多样的多源多维大数据,提出不同业务应用场景的数据治理和融合技术。方法/过程以多源多维大数据融合在应对重大突发公共卫生事件的实践为例,重点阐述多源多维大数据融合面临的挑战、应用架构设计、技术集成、实施路径、应用场景等内容,分析应用效果和存在的不足。结果/结论通过智能化多源多维大数据融合技术的设计,实现对千亿级别高频次更新的实时监听、读取、治理和交互关联,探索有效应对突发公共应急事件的智慧化路径。

面向新型电力系统智能化提效的多源异构数据融合技术研究

新型智能计量设备采集的异构数据揭示了电网系统内部各构件,以及电网及其他智慧城市系统间的互动关系。针对结合多种数据进行关联分析的场景,提出一种面向多源异构数据的数据融合方法。该方法基于时域-空域的高斯过程模型,并通过层次化的贝叶斯方法简化求解。模型结合电力数据及外部数据,实现对电网缺失数据的信息补充,从而提升电网系统智能化任务的性能。以电网的负载预测这一典型任务为例,验证了该方法在实际场景中的应用潜力。

公共安全领域多源异构数据融合技术与应用

针对公共安全领域涉及数据种类多、数据来源各异无法形成高效数据组织形式的问题,本文提出了一种公共安全领域多源异构数据融合技术,通过规范化治理流程,对公共安全领域海量多源异构数据进行融合治理,形成标准化存储,构建体系化的数据资源组织形式,为公共安全领域业务应用的开展提供数据支撑。

基于K-medoids聚类的异构环境多源烟草物流数据集成方法研究

由于烟草物流行业在运营过程中涉及的数据来源极其广泛且多样,数据不仅格式各异、结构复杂,而且往往分散存储在不同的信息系统中,导致物流数据在集成的过程中,出现数据吞吐量较低的现象。针对上述现象,提出基于K-medoids聚类的异构环境多源烟草物流数据集成方法。通过欠采样平衡类别分布,利用数据相关性和阈值清洗剔除冗余信息,提高异构环境多源烟草物流数据质量,设计基于K-medoids聚类的烟草物流数据集成框架,使用迁移学习动态调整源域权重以优化目标域聚类性能,引入带有相似性约束的新数据点作为初始聚类中心,实现异构环境多源烟草物流数据的有效集成。实验结果表明,设计方法通过聚类算法能够将来自不同数据源的数据进行有效分组和整合,降低数据处理的复杂性,提高数据集成的吞吐量。

智能化矿山数据治理实践与策略研究

针对煤炭行业在推进智能化矿山建设中面临的许多数据治理挑战,如数据标准不统一、多源异构数据融合难、数据质量控制不足及标准更新滞后等,提出了一套智能化矿山数据治理策略与实践方案。通过分析现状,明确了构建统一、协调、高效数据标准体系的重要性,旨在提升数据质量,加速数据流通与价值挖掘,支撑煤炭行业的数字化转型。设计了涵盖数据来源唯一性、数据结构规范性以及数据属性标准化的数据标准体系,并通过EIP模型的应用,实现了数据对象模型库的统一,解决了系统间数据共享与联动的难题。此外,方案还涉及数据融合处理机制,通过标准化接口和数据服务总线,有效整合了多源异构数据,实现了数据的分层分类管理,提升了数据处理的时效性和效率,为智能化矿山的数据治理提供了实践指导与策略框架,有助于煤矿实现安全、高效的生产运营。

一种工业多源异构大数据组织模型和集成框架的研究与应用

本文旨在设计一种面向工业多源异构大数据环境的组织模型和集成框架,为工业生产领域的软件开发者和决策者提供大数据集成的参考体系结构。该框架能够对结构化和非结构化数据进行有效管理,提供复合视图显示、全文检索、数据共享等功能,为软件系统开发提供数据访问接口,实现海量多源异构数据以及应用业务的聚合和扩展,使软件开发者在此框架基础上实现生产管理和决策系统的应用。

电网多源异构缺失数据最优投影整合算法研究

电网多源异构数据分散性较强,导致数据随机缺失问题严重,影响电网整体运行的管理质量。研究了一种电网多源异构缺失数据最优投影整合算法。首先,引入拉格朗日差值方法填充电网多源异构数据中的缺失值。其次,采用经验模态分解方法消除电网多源异构数据中的噪声。最后,建立投影指标函数,将高维电网多源异构数据经过投影处理后转变为一维的投影值。计算模糊聚类迭代适应度,利用混沌文化差分进化算法寻找电网多源异构数据的最优投影方向,完成缺失数据的整合。试验结果表明,所提算法的数据去噪效果较好,去噪后电网多源异构数据并未出现失真现象,并且能够在提高整合精度的同时确保算法运行的稳定性。所提算法数据整合精度受数据缺失的影响较小。该研究能够提高电网多源异构数据的质量,有助于提高电网运行管理水平。