Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things(IoT)-based big data management and analysis in cloud environments.Challenges arising from the fields of machine learning in cloud infrastructures,artificial intelligence techniques for big data analytics in cloud environments,and federated learning cloud systems are elucidated.Additionally,reinforcement learning,which is a novel technique that allows large cloud-based data centers,to allocate more energy-efficient resources is examined.Moreover,we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework(EEIBDM)established outside of every user in the cloud.IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room tem-peratures.Furthermore,we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework.Finally,future directions for the expansion of this research are discussed.

Research on data pre-deployment in information service flow of digital ocean cloud computing

Data pre-deployment in the HDFS （Hadoop distributed file systems） is more complicated than that in traditional file systems. There are many key issues need to be addressed, such as determining the target location of the data prefetching, the amount of data to be prefetched, the balance between data prefetching services and normal data accesses. Aiming to solve these problems, we employ the characteristics of digital ocean information service flows and propose a deployment scheme which combines input data prefetching with output data oriented storage strategies. The method achieves the parallelism of data preparation and data processing, thereby massively reducing I/O time cost of digital ocean cloud computing platforms when processing multi-source information synergistic tasks. The experimental results show that the scheme has a higher degree of parallelism than traditional Hadoop mechanisms, shortens the waiting time of a running service node, and significantly reduces data access conflicts.

Intelligent Digital Envelope for Distributed Cloud-Based Big Data Security

Cloud computing offers numerous web-based services.The adoption of many Cloud applications has been hindered by concerns about data security and privacy.Cloud service providers’access to private information raises more security issues.In addition,Cloud computing is incompatible with several industries,including finance and government.Public-key cryptography is frequently cited as a significant advancement in cryptography.In contrast,the Digital Envelope that will be used combines symmetric and asymmetric methods to secure sensitive data.This study aims to design a Digital Envelope for distributed Cloud-based large data security using public-key cryptography.Through strategic design,the hybrid Envelope model adequately supports enterprises delivering routine customer services via independent multi-sourced entities.Both the Cloud service provider and the consumer benefit from the proposed scheme since it results in more resilient and secure services.The suggested approach employs a secret version of the distributed equation to ensure the highest level of security and confidentiality for large amounts of data.Based on the proposed scheme,a Digital Envelope application is developed which prohibits Cloud service providers from directly accessing insufficient or encrypted data.

MEDICLOUD:a holistic study on the digital evolution of medical data

The Corona Virus Disease 2019(COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches,thereby highlighting a scenario where artificial intelligence(AI) merges with traditional Chinese medicine(TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors,nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors’ authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users’ lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.

Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model

With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.

Reversible Data Hiding for Medical Images in Cloud Computing Environments Based on Chaotic Hénon Map

Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.

Block Level Data Integrity Assurance Using Matrix Dialing Method towards High Performance Data Security on Cloud Storage

Data outsourcing through cloud storage enables the users to share on-demand resources with cost effective IT services but several security issues arise like confidentiality, integrity and authentication. Each of them plays an important role in the successful achievement of the other. In cloud computing data integrity assurance is one of the major challenges because the user has no control over the security mechanism to protect the data. Data integrity insures that data received are the same as data stored. It is a result of data security but data integrity refers to validity and accuracy of data rather than protect the data. Data security refers to protection of data against unauthorized access, modification or corruption and it is necessary to ensure data integrity. This paper proposed a new approach using Matrix Dialing Method in block level to enhance the performance of both data integrity and data security without using Third Party Auditor (TPA). In this approach, the data are partitioned into number of blocks and each block converted into a square matrix. Determinant factor of each matrix is generated dynamically to ensure data integrity. This model also implements a combination of AES algorithm and SHA-1 algorithm for digital signature generation. Data coloring on digital signature is applied to ensure data security with better performance. The performance analysis using cloud simulator shows that the proposed scheme is highly efficient and secure as it overcomes the limitations of previous approaches of data security using encryption and decryption algorithms and data integrity assurance using TPA due to server computation time and accuracy.

CloudCD:基于云计算平台的交往社区发现系统

交往社区发现已经成为社会网络分析中一个重要的研究课题.借助于社区发现技术,可以挖掘网络中更多有价值的、可靠的隐含信息,有利于用户做出更准确、更有价值的判断和决策.但是随着社交网络的信息量日益庞大,传统技术已经很难满足对海量数据的处理需求.为有效处理大规模网络数据,借助于云计算平台,实现了一种基于云计算平台的社区发现系统———CloudCD;考虑到社区中各节点的重要性差别,CloudCD首先挖掘社区中处于核心地位的节点,然后再扩展得到附属节点,用户可以通过设定扩展步长来获得不同层次粒度的社区结构;另外,系统也充分考虑了社区重叠的状况.通过不同粒度社区挖掘、社区重叠、系统加速比3个实验,展示了系统的效果.

Managing Computing Infrastructure for IoT Data

Digital data have become a torrent engulfing every area of business, science and engineering disciplines, gushing into every economy, every organization and every user of digital technology. In the age of big data, deriving values and insights from big data using rich analytics becomes important for achieving competitiveness, success and leadership in every field. The Internet of Things (IoT) is causing the number and types of products to emit data at an unprecedented rate. Heterogeneity, scale, timeliness, complexity, and privacy problems with large data impede progress at all phases of the pipeline that can create value from data issues. With the push of such massive data, we are entering a new era of computing driven by novel and ground breaking research innovation on elastic parallelism, partitioning and scalability. Designing a scalable system for analysing, processing and mining huge real world datasets has become one of the challenging problems facing both systems researchers and data management researchers. In this paper, we will give an overview of computing infrastructure for IoT data processing, focusing on architectural and major challenges of massive data. We will briefly discuss about emerging computing infrastructure and technologies that are promising for improving massive data management.

Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications

In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion.Employees,however,have been exposed to different security risks because of working from home.Moreover,the rapid global spread of COVID-19 has increased the volume of data generated from various sources.Working from home depends mainly on cloud computing(CC)applications that help employees to efficiently accomplish their tasks.The cloud computing environment(CCE)is an unsung hero in the COVID-19 pandemic crisis.It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data.Despite the increase in the use of CC applications,there is an ongoing research challenge in the domains of CCE concerning data,guaranteeing security,and the availability of CC applications.This paper,to the best of our knowledge,is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE.Additionally,this paper also highlights the security risks of working from home during the COVID-19 pandemic.

大庆油田CIFLog测井数智云平台建设应用实践

针对大庆油田生产中测井数据量大、类型多和数据来源复杂等问题,以中国石油天然气集团有限公司大型测井处理解释软件CIFLog为基础,以业务需求为主导,采用微服务架构和测井分布式云计算技术体系,研发测井大数据存储管理、中间服务层和云端测井处理解释应用等新功能,形成了大庆油田测井数智云应用平台。目前,平台已全面安装部署到大庆油田相关单位,应用效果显著。特别在大庆油田智能决策中心,平台直接用于重点水平井随钻地质导向的现场决策,大幅提升了Ⅰ类储层的钻遇率。未来平台将重点围绕新功能研发、油田数智化应用场景建设和标准化技术体系构建等开展工作,并将取得的成果及时推广复制到西南油田、塔里木油田等油气田。CIFLog云平台作为中国油气工业软件数智化建设应用的先行典范,必将发挥越来越重要的示范引领作用。

数字文旅视域下乡村美食旅游开发模式创新研究——以四川省为例

数字文旅是新时代乡村美食旅游高质量发展的重要引擎,对乡村美食旅游的供给侧、需求侧和资源配置有变革性影响。将四川省乡村美食旅游开发置于数字文旅视域下,针对开发现状和开发困境,提出应从大数据文旅、云计算文旅、物联网文旅三个视角出发,创新构建“大云物”乡村美食旅游开发模式,助力乡村美食旅游数字化转型升级发展。

高等教育的数字化转型与关键流程

高等教育数字化转型是大数据、互联网、云计算、人工智能等数字技术的集群式创新及其与高等教育的深度融合。通过梳理数字化转型与高等教育的关系、高等教育数字化转型目标,我国高等教育的数字化转型之路进行的分析,从战略和运行两个层次阐述了高等教育数字化转型的关键流程,以期通过多方面的技术创新,真正实现大数据驱动教育智能化,回归教育本质,提升教育品质。

数字化技术在结构健康监测中的应用与探索

随着基础设施建设步伐的加快,工程结构的安全性和可靠性愈发受到重视。结构健康监测作为土木工程领域的关键技术,通过数字化技术实现了自动化、实时化和智能化的监测,显著提升了监测效率和准确性。论文综述了结构健康监测的基本概念、关键技术及其应用领域。BIMBase 等数字化平台整合了传感器数据与分析结果,为工程安全提供了有力的数据支撑。物联网技术则实现了实时连续监测,降低了人力成本。然而,结构健康监测仍面临数据复杂性和实时性处理的挑战。大数据分析与智能诊断技术成为研究热点,为结构性能评估和故障诊断提供了新途径。未来,人工智能和区块链技术的融合将为结构健康监测带来革命性变革,推动土木工程领域向更安全、更高效的方向发展。

无线传感器网络课程的教学实践分析

阐述无线传感器网络课程的特点及教学方法设计。提出融入小组讨论、动手实验和模拟等互动策略,利用在线平台、虚拟实验室和多媒体资源等现代技术工具,提升现代有效的学习体验。

云计算与大数据在核电大修领域的应用研究

核电厂周期性的机组大修是保障核电站正常运行过程中最重要的检修活动,单次大修活动涉及的系统、设备非常多,人员范围广、数量多,业务复杂程度高,如何将众多不同系统产生的数据和大修业务活动进行融会贯通,一直是核电厂不断进行探索的难题。通过对云计算与大数据技术在核电数字化大修领域的深入应用研究,提出一种通过云计算及大数据技术解决核电数字化大修过程中大量数据并发采集、汇聚、加工、计算的问题的解决方案。

数字技术在企业财务管理中的应用

阐述数字技术的特点,探讨数字技术在企业财务管理中的应用,包括云计算、人工智能、大数据、区块链、物联网技术在财务管理中的应用,从而为企业财务管理数字化转型提供新的思路和方法。

智能电网中的能源监测与优化策略分析

阐述能源监测技术和方法,对数字化电力工程中的能源监测方案进行评估和比较,针对能源监测中的海量、多样性、实时性数据特点,提出一种基于大数据和云计算的能源监测与分析平台设计方案。

大数据与云计算在数字政协中的应用与实践

大数据技术在数字政协中可以应用于多个方面,其中,最重要的应用之一是对政协数据的收集和分析。通过对政协数据的收集和分析,可以更好地了解政协委员的意见和建议,为政策制定提供更加科学和准确的参考。该文将探讨大数据与云计算在数字政协中的应用与实践。

绿色建筑智能化监控平台的研究——数字孪生技术的应用

数字孪生技术在建筑智能化监控中的应用主要体现在运营与管理上,通过建立与真实世界相同的数字副本,使实体建筑与其相应的数字模型相连,实现对各种运营和管理全过程的监测、分析和管控。通过对比虚拟模型和实际建筑的能源数据,找出能源利用效率低下的环节,并提出优化建议。数字孪生技术在楼宇安全管理方面的应用具有广泛前景和潜力,通过实时监测与预警、设备健康管理、能耗优化管理、安全风险分析、应急预案演练、人员行为监控、智能决策支持和数据集成与分析等手段,可以显著提高建筑楼宇的安全管理水平,保障人们的生命财产安全。