Research on the Application of Big Data and Cloud Computing Technology in Smart Campus

The current education field is experiencing an innovation driven by big data and cloud technologies,and these advanced technologies play a central role in the construction of smart campuses.Big data technology has a wide range of applications in student learning behavior analysis,teaching resource management,campus safety monitoring,and decision support,which improves the quality of education and management efficiency.Cloud computing technology supports the integration,distribution,and optimal use of educational resources through cloud resource sharing,virtual classrooms,intelligent campus management systems,and Infrastructure-as-a-Service(IaaS)models,which reduce costs and increase flexibility.This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses,showing how these technologies can contribute to the development of smart campuses,and laying the foundation for the future innovation of education models.

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.

Improving Performance of Cloud Computing and Big Data Technologies and Applications

Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.

Cloud Computing and Big Data: A Review of Current Service Models and Hardware Perspectives

Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application;the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.

Research on the Library Enterprise Development Prospects and Challenges under the Environment of Big Data and Cloud Computing

In this paper, we conduct research on the library development prospects and challenges under the environment of big data and cloud computing. Increasingly nervous and public libraries are facing funding, resources construction pace slow or stagnant difficult situation, readers to the library cause in the new times challenge. Big data era has quietly come, for the knowledge storage, use and development as own duty, the library, how to improve the ability of handling large amounts of growth literature is urgent. Our methodology solves the issues well which will be meaningful.

Fog-IBDIS:Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN

The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.

Big Data of Home Energy Management in Cloud Computing

A smart grid is the evolved form of the power grid with the integration of sensing,communication,computing,monitoring,and control technologies.These technologies make the power grid reliable,efficient,and economical.However,the smartness boosts the volume of data in the smart grid.To obligate full benefits,big data has attractive techniques to process and analyze smart grid data.This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid.The offered framework comprises of the following four layers:(i)Data source layer,(ii)Data transmission layer,(iii)Data storage and computing layer,and(iv)Data analysis layer.As a proof of concept,the framework is simulated by taking the dataset of three cities of the Pakistan region and by considering two cloud-based data centers.The results are analyzed by taking into account the following parameters:(i)Heavy load data center,(ii)The impact of peak hour,(iii)High network delay,and(iv)The low network delay.The presented framework may help the power grid to achieve reliability,sustainability,and cost-efficiency for both the users and service providers.

Design and Implementation of a Project Management System Based on Product Data Management on the Baidu Cloud Computing Platform

Aiming at enterprises without commercial project management systems(PMS)in a product data management(PDM)environment,and using a cloud computing platform,this research analyses the business process and function of complex product project management in PDM,and proposes a PMS-based organizational structure for such a project.This model consists of a task view,user view,role view,and product view.In addition,it designs the function structure,E-R model and logical model of a PMS database,and also presents an architecture based on the Baidu cloud platform,describes the functions of the Baidu App Engine(BAE),establishes the overall PMS software architecture.Finally,it realizes a revised product design project by using EasyUI,J2EE and other related technologies.Practice shows that the PMS designed for PDM has availability,scalability,reliability and security with the help of the Baidu cloud computing platform.It can provide a reference for small-and medium-sized enterprises seeking to implement information systems with high efficiency and at low cost in the age of big data.

Privacy-Preserving Deep Learning on Big Data in Cloud

In the analysis of big data,deep learn-ing is a crucial technique.Big data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage areas.Nev-ertheless,there is a contradiction between the open nature of the cloud and the demand that data own-ers maintain their privacy.To use cloud resources for privacy-preserving data training,a viable method must be found.A privacy-preserving deep learning model(PPDLM)is suggested in this research to ad-dress this preserving issue.To preserve data privacy,we first encrypted the data using homomorphic en-cryption(HE)approach.Moreover,the deep learn-ing algorithm’s activation function—the sigmoid func-tion—uses the least-squares method to process non-addition and non-multiplication operations that are not allowed by homomorphic.Finally,experimental re-sults show that PPDLM has a significant effect on the protection of data privacy information.Compared with Non-Privacy Preserving Deep Learning Model(NPPDLM),PPDLM has higher computational effi-ciency.

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.

Big Data and Data Science:Opportunities and Challenges of iSchools

Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

Secure Big Data Storage and Sharing Scheme for Cloud Tenants

The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud.In this paper,we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers.Instead of protecting the big data itself,the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function.Analysis,comparison and simulation prove that the proposed scheme is efficient and secure for the big data of Cloud tenants.

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.

Research on the Big Data Cloud Computing Based on the Network Data Mining

The big data cloud computing is a new computing mode,which integrates the distributed processing,the parallel processing,the network computing,the virtualization technology,the load balancing and other network technologies.Under the operation of the big data cloud computing system,the computing resources can be distributed in a resource pool composed of a large number of the computers,allowing users to connect with the remote computer systems according to their own data information needs.

Research on the big data feature mining technology based on the cloud computing

The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.

Fine-Grained Access Control for Big Data Based on CP-ABE in Cloud Computing

In Cloud Computing, the application software and the databases are moved to large centralized data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings many new security challenges, which have not been well solved. Data access control is an effective way to ensure the big data security in the cloud. In this paper,we study the problem of fine-grained data access control in cloud computing.Based on CP-ABE scheme,we propose a novel access control policy to achieve fine-grainedness and implement the operation of user revocation effectively.The analysis results indicate that our scheme ensures the data security in cloud computing and reduces the cost of the data owner significantly.

Big Data Storage Architecture Design in Cloud Computing

To solve the lag problem of the traditional storage technology in mass data storage and management,the application platform is designed and built for big data on Hadoop and data warehouse integration platform,which ensured the convenience for the management and usage of data.In order to break through the master node system bottlenecks,a storage system with better performance is designed through introduction of cloud computing technology,which adopts the design of master-slave distribution patterns by the network access according to the recent principle.Thus the burden of single access the master node is reduced.Also file block update strategy and fault recovery mechanism are provided to solve the management bottleneck problem of traditional storage system on the data update and fault recovery and offer feasible technical solutions to storage management for big data.

DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data world.This approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the cloud.The IoE-based cloud computing services are located at remote locations without the control of the data owner.The data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security capabilities.The lack of knowledge about security capabilities and control over data raises several security issues.Deoxyribonucleic Acid(DNA)computing is a biological concept that can improve the security of IoE big data.The IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher algorithms.This paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access issues.The experimental results illustrated that DNACDS performs better than other DNA-based security schemes.The theoretical security analysis of the DNACDS shows better resistance capabilities.

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-ofthe-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.