Data-Oriented Operating System for Big Data and Cloud

Operating System(OS)is a critical piece of software that manages a computer’s hardware and resources,acting as the intermediary between the computer and the user.The existing OS is not designed for Big Data and Cloud Computing,resulting in data processing and management inefficiency.This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes.The proposed algorithm utilizes the performance benefits from the improved Input/Output(I/O)performance.The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by reducing memory access overheads in conventional data management.The OS incorporates a data-oriented design to“modernize”various Data Science and management aspects.The resulting OS contains a basic input/output system(BIOS)bootloader that boots into Intel 32-bit protected mode,a text display terminal,4 GB paging memory,4096 heap block size,a Hard Disk Drive(HDD)I/O Advanced Technology Attachment(ATA)driver and more.There are also I/O scheduling algorithm prototypes that demonstrate how a simple Sweeping algorithm is superior to more conventionally known I/O scheduling algorithms.A MapReduce prototype is implemented using Message Passing Interface(MPI)for big data purposes.An attempt was made to optimize binary search using modern performance engineering and data-oriented design.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Big Data Analytics of Taxi Operations in New York City

As a global financial center, the transportation system in New York City (NYC) has always been studied from various aspects. Since 2009, NYC Taxi and Limousine Commission have made public the information on NYC taxi operations, offering an opportunity for detailed analysis. Thus, this research project investigates taxi operations in New York City based on big data analysis. The correlation between taxi operations and different types of weather, including precipitation, snow depth, and snowfall is discussed in this paper. The research also evaluates taxi trip distribution in each NTA area using Geopandas, and presents its density on an NYC map.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Research on the Database Technology under the Operation of the IOT Huge Amounts of Data

In this paper, we conduct research on the database technology under the basic operation of the IOT huge amounts of data. Spatial data service is the process of basic customer application system service request, directory service system according to the request is mapped into physical addresses, according to the data block physical disk server address block of data read from the disk in parallel and sent to the client the process of application system. In addition, spatial data distribution system should possess unloading, transfer, replication, and optimization of spatial data, and other functions, to achieve comprehensive management and maintenance of spatial data. Under this basis, we propose the enhanced database technology that optimizes the operation with better performance.

Predictive Simulation of External Truck Operation Time in a Container Terminal Based on Traffic Big Data

The operation time of external trucks in a container terminal is one of port operation key performance indicators concerned by port operators,external truck operators and related government authorities.With the traffic big data combined with the operation characteristics of the container terminal,the system dynamics method is used to build the simulation model of the operation system for external trucks.The simulation results of the operation time of external trucks are consistent with the actual situation,which provides an effective way to eliminate the“black box”of the operation time of the external trucks.The model can also be applied in multiple scenarios by using the traffic big data,and the simulation results can be adopted by the relevant organizations.

Operational Climate Prediction in the Era of Big Data in China:Reviews and Prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things.With the development of climate observing systems,particularly satellite meteorological observation and high-resolution climate models,and the rapid growth in the volume of climate data,climate prediction is now entering the era of big data.The application of big data will provide new ideas and methods for the continuous development of climate prediction.The rapid integration,cloud storage,cloud computing,and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively,thus predicting the future climate more accurately.This paper describes the application status of big data in operational climate prediction in China;it analyzes the key big data technologies,discusses the future development of climate prediction operations from the perspective of big data,speculates on the prospects for applying climatic big data in cloud computing and data assimilation,and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

Review on Application of Artificial Intelligence in Civil Engineering

In last few years,big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques.However,until now,there has been no comprehensive review on its applications in civil engineering.To fill this gap,this paper reviews the application and development of artificial intelligence in civil engineering in recent years,including intelligent algorithms,big data and deep learning.Through the work of this paper,the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be known.It is shown that the studies of artificial intelligence in civil engineering mainly focus on structural maintenance and management,and the design optimization.

基于DataOps的运营商大数据平台探索

在数字经济时代下,数据成为运营商的核心资产,DataOps是释放数据价值的有效模式,而大数据平台是实现DataOps的技术基础。通过对DataOps与大数据平台的发展进行梳理,阐述了DataOps的内涵,提出DataOps应用于运营商大数据平台的方法,并构建了基于DataOps的运营商大数据平台架构。

Capacity Openness Boosts Digitization Transformation of Telecom Operators

With the emergence of digital transformation success stories,more and more enterprises are launching digital solutions to improve business efficiency or achieve optimal revenue growth.The traditional communication market tends to be saturated,and the digital transformation of telecom operators is imminent.This article combining the development trend of telecom operators’product business and the application situation of the pilot summarizes the innovative application categories that telecom operators continue to launch based on their own advantages and in response to the needs of the industry.This article focuses on the transformation of security capabilities in communication services,big data,Internet of Things,artificial intelligence,unified authentication and network information security,and deeply analyzes the business characteristics and application modes from the aspects of resource integration,open platform construction,new connection construction,and innovative business development.This article puts forward suggestions for the security development of telecom operators,and provides basic research content for the subsequent research on the innovative application of telecom operators’capability openness.

基于BIM和语义网的轨道智能运维管理方法

建筑信息模型(BIM)技术对提高轨道运维管理效率具有重要的推进作用。然而,不同的检查和维护活动产生的数据异构性高、时空关系复杂,阻碍了BIM解释和整合数据的进程。为此,开发了一个基于工业基础类(IFC)和语义Web技术的轨道运维本体(TOMO),其具有3个功能:①基于轨道运维生命周期的应用需求,简化BIM模型信息;②引入映射规则,建立数据提取与转换模块,集成多源异构数据,结构化定义数据之间复杂的时空关系;③结合数据驱动技术,研究轨道精调智能优化的方法,提供弹性决策支持。最后,以某高速铁路静检数据为例,验证了该框架的有效性与实用性,对于促进领域数据互操作性、降低运维人员劳动强度和提高运维管理智能化程度具有实际的工程指导意义。

ITMA ASIA+CITME2022上新一代信息技术应用浅析

探讨ITMA ASIA+CITME2022上众多新一代信息技术的应用情况。从车间物流数字孪生、原棉精细化管理、细纱机单锭监测、设备远程运维等不同角度,梳理了展会上新一代信息技术应用场景及解决方案。指出:纺机企业研发方向主要面向高效节能、高附加值纺织品的数字化、自动化和智能化生产与质量管控;新一代信息技术使得纺织产业数字化升级更加快速和高效,纺织生产整体智能化水平明显提升;基于工业互联网的纺织产业新生态正在加快形成。认为:新一代信息技术与纺织产业的融合应用,推动了产品研发、制造、服务等环节的协同创新,助力企业实现提质增效与高质量发展的战略目标。

基于Pandas进行关键部位维修周期分析

以关键部位维修周期分析为例,详细阐述了基于Pandas对Excel表格进行数据导入、清洗无效行、清洗无效列、数据透视、数据合并重塑、排序与排名、分组运算的实现过程.该方法解决了关键部位维修周期分析中的存在数据量大、基础数据不规范、计算过程复杂等难点,实现了关键部位维修周期分析的自动化.该方法也适用于人事数据、财务数据的年度汇总,具有良好的实用性与推广价值.

烟草制造业务系统智能运维平台的设计与应用

随着国家“两化融合”战略的提出和实施,烟草企业积极响应并进行全面的信息化建设,信息技术在烟草企业的深入发展,信息化业务系统以及数据量呈指数态势上升,同时烟草企业内部各个业务系统相对独立,在系统间缺乏有效的数据共享和协同,存在系统竖井的问题,业务端与设备端运维缺乏有效的协同支持,为运维人员的日常工作带来了很大的挑战。本文研究智能运维在烟草企业的落地场景,通过应用性能指标异常智能化分析、系统故障的智能化判断与辅助、日志集中管理与分析以及系统性能容量预测四大场景,设计以烟草制造业务与IT资源双稳定为运维视角的智能运维平台。

大数据技术在图书馆运营中的运用分析

新时代下,现代科学技术日益成熟,大数据产业也随之蓬勃发展,大数据技术水平越来越强大。在图书运营中引入并合理使用大数据技术,是目前图书馆建设的重点。满足不同用户所需,是图书馆运营质量及服务能力的决定性因素。对此,以大数据技术对图书馆运营的影响及具体应用为切入点,重点分析了目前图书馆运营中大数据技术应用现状,并提出了优化运用的对策。

基于SDES和双轮差错控制的大数据集成安全系统

针对大数据传输中的数据机密性、完整性和数据丢失等问题,提出一种基于简化数据加密标准(Simplified Data Encryption Standard,SDES)和双轮差错控制的大数据集成安全系统。使用SDES加密算法生成加密字符串,并设计意外数据丢失备份系统以提高机密性和防止意外数据丢失。基于双轮差错控制以较低的空间开销控制传输过程中包含的任意数量的离散或连续错误位,基于固定长度编码(Fixed Length Coding,FLC)的无损压缩技术来减少数据开销。该算法具有较高的AE值、熵和压缩百分比,具有提供更高的数据机密性和完整性的潜力。

大数据时代图书馆管理策略初探

随着新一代信息技术的发展,世界已经进入到了大数据时代。大数据、数字技术、云计算及网络应用技术的深入发展,改变了图书馆的传统经验型管理模式,信息技术使图书馆经营管理质量获得了较大幅度的提升。大数据作为现代图书馆的核心技术,给图书馆管理带来了深刻的影响和变化。本文详细阐述了大数据时代对图书馆服务管理的影响因素,并有针对性地提出了大数据时代提升图书馆管理质量的对策,以供相关从业人员参考借鉴。

工业大数据建模方法及生产线运行状态监控技术研究

随着我国科学技术水平的不断提升,大数据智能技术被广泛应用于工业生产中,有效推进了我国工业化建设的进程,与此同时,在工业生成过程中,数字化生产线的建设是提高工业生产效率,实现工业智能生产的关键环节。然而,在实际工业生产中,大部分车间智能化建设水平还比较低,监测工业制造流程的监控系统存在实时性较差以及准确性不足等问题。因此,文章基于大数据技术背景,利用智能信息通信技术,构建以大数据建模为基础的实时生产线运行状态监控系统,实现对生产流程的全面实时监测,加强工业生产的透明化,从而有效提高工业生产运营的管理水平。

基于信创环境的水利智搜优化设计与研究

为提高水利智搜数据采集效率、个性化推荐程度、协调运维能力、信创环境适配程度,向社会公众提供便捷高效的水利信息检索服务,结合核心技术自主可控的必然要求,研究运用数据采集、搜索推荐模型等智能化处理技术,进一步优化搜索算法,加强数据信息采集。同时,通过优化水利智搜平台设计,基于信创环境适配优化,采用集成式、场景式和交互式一体化数据呈现方式,提升支撑、统计分析、搜索和聚合能力,实现精准化推荐和协同化运维。基于信创环境的水利智搜优化设计与研究,可为水利行业智能化信息检索服务提供经验借鉴。

智慧教育平台体系运行监测研究

随着国家教育数字化战略行动的逐步深入,跨层级、跨地域的智慧教育平台体系规模持续增长,如何有效开展其运行监测,助力教育大数据中心建设,已成为推进教育数字化的重要突破口。对此,文章首先概括性地介绍了智慧教育平台体系的发展历程与运行监测现状;接着,引入监测信使机制和采集数据模型,构建了智慧教育平台监测模型,提出了智慧教育平台体系的运行监测流程;随后,文章设计了包含“四横四纵两端”的智慧教育平台体系运行监测系统技术架构;最后,文章阐释了智慧教育平台体系运行监测大数据的应用思路。文章从多个角度对智慧教育平台体系运行监测进行了研究,可为教育数字化应用体系的主管单位构建运行监测系统、探索教育大数据创新应用提供技术思路。