Cloud Computing Data Research Articles

Exploring knowledge management technologies to enhance sustainability and mitigate technostress from a collaborative perspective

Purpose The relevance of digital technology and knowledge management (KM) has been increasing continuously owing to their importance in sustainability. However, research on which technology leads to collaboration in KM and how it affects sustainability is lacking. Therefore, this study aims to analyze knowledge collaboration management technologies for sustainable growth based on academic literature and technology patents. Design/methodology/approach This paper analyzed current technology and academic trends in KM using BERTopic, an artificial intelligence natural language processing model, and proposed future promising candidate technologies and potential application areas in terms of collaboration and sustainability. Findings From the textual data of research papers and patents, this paper extracted 10 and 47 major topics, respectively. Moreover, five potential and feasible candidate patents along with their applicable fields were recommended. Originality/value The strength of this study is its analysis of the technologies affecting collaboration and sustainability in KM. This paper found that new digital technologies (i.e. artificial intelligence, cloud computing and big data) impact organizations’ KM and have the potential for collaboration. Digital technologies have a positive impact on corporate smartness directly and sustainability and inclusion indirectly. This paper argued that organizations should understand and identify various promising technological factors and apply them to their KM from a collaboration perspective.

Big Data in Cloud Computing

The convergence of Big Data and cloud computing has revolutionized the way organizations process, store, and analyze large datasets. This paper explores the synergistic relationship between these two transformative technologies, highlighting their impact on business operations, decision-making, and innovation. By leveraging cloud platforms, enterprises can harness the scalability, flexibility, and cost-efficiency of distributed computing to manage vast volumes of data generated from diverse sources. Cloud-based big data analytics enables real-time insights, which drive strategic actions, improve customer experiences, and foster competitive advantages. The research delves into key cloud architectures, data management techniques, and analytics tools that underpin big data solutions, while addressing challenges such as data security, privacy, and compliance. Furthermore, the study reviews industry use cases across various sectors, illustrating how big data in cloud environments enhances productivity and innovation. The findings underscore the pivotal role of cloud computing in unlocking the full potential of big data, offering a roadmap for businesses aiming to capitalize on data-driven strategies.

Research on the Legal Risks of Digital Currency in the Big Data Environment

With the widespread application and growth of blockchain technology, cloud computing, and big data analytics in the financial industry, digital currencies have emerged. However, relevant regulations specifically targeting digital currencies have not been officially promulgated yet. In the process of promoting digital currencies, legal risks such as unclear boundaries of compensation liability, unclear ownership, potential privacy breaches, and financial regulatory challenges still need to be overcome. Therefore, it is essential to prioritize the prevention and management of legal risks associated with digital currencies in the big data environment, ensuring the smooth circulation and use of digital legal currencies across regions and promoting the internationalization and legalization of China’s digital legal currencies.

Electric vehicles charging infrastructure framework using internet of things

Electric vehicles (EVs) sales have grown rapidly recently, and more growth is expected over the coming years. A challenging problem arises when managing different battery requirements of moving EVs through reliable Charging Stations (CSs). Current concerns for EV users are long waiting lines at CSs and dropping below a predefined battery capacity limit. For this reason, this paper proposes an Internet of Things (IoT)-based EV charging scheduling system, which with the use of IoT technologies, decides the optimal assignment between EVs and Charging Points (CPs) located at different CSs at given time t. By using cloud computing and real time data such as number of EVs, number of CSs, number of CPs at different CSs … etc; the scheduling controller uses a recursive algorithm to generate all possible scenarios, and then shares the optimal assignment (that minimizes the average waiting time and fulfill battery constraints and charging needs) with all EVs. To test the validity of the IOT based scheduling system, sensitivity analysis by running different scenarios (pertaining to different parameters) was conducted. The different scenarios were compared to a base scenario where the system was not used and real-life random assignment is considered. The different run scenarios show superiority over the base scenario in terms of average waiting time (WT) and battery capacity threshold. For example, in the base scenario, violation of battery capacity threshold occurred 9.1% of the time, making random selection an unreliable choice versus no violations when the IOT scheduling system is used. Also, all tested scenarios under the IOT scheduling system show shorter average WT compared to the base scenario. For instance, scenarios 2 and 3 show more than 35% and 55% decrease in WT compared to the base scenario.

Analysis of the Digital Transformation Path of Chinese E-commerce Companies

Digital technology has revolutionized China’s e-commerce sector, changing company operations and client relationships. This article analyzes the impact of digital transformation on online purchasing patterns. This article concentrated on the important of cloud computing, AI, and big data have become essential to online retail tactics. Digitalization is fueling economic development and innovation in China as its digital economy grows and impacts GDP. Digital technologies enable e-commerce and other industrial sectors to interact easily, improving operational efficiency and market reach. Moving from isolated firm models to connected platforms is a sign of a more responsive and integrated economy. This integration fosters a value model of co-creation in which stakeholders (including companies) collaborate to develop product and service offerings, improving customer experience and operational productivity. The route to digital maturity is not without challenges. E-commerce companies struggle with inefficient logistics, costly digital transformation, and a dearth of competent digital workers, as this study shows. Companies must make dramatic organizational and technological adjustments to overcome these constraints and maximize digital technologies. The analysis concludes that China’s e-commerce market will depend on enterprises’ digital adoption. E-commerce enterprises must embrace ongoing learning and development and remove digital integration barriers to compete in today’s dynamic online industry. The study illuminates digital transformation and how it will impact e-commerce.

Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies

Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.

Digital transformation technologies for conveyor belts predictive maintenance: a review

The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era.

Security issues analysis based on big data in cloud computing

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.

Optimizing data management in healthcare: Lessons from clinical trials and beyond

This study comprehensively explores the optimization of data management in healthcare by drawing on lessons learned from clinical trials and extending these insights to broader healthcare contexts. The study's purpose is to examine the challenges, strategies, and technological innovations that are essential for effective data management in modern healthcare systems. Utilizing a detailed literature review, the study identifies key methodologies from clinical trials that have successfully enhanced data integrity, accuracy, and security, such as the implementation of electronic data capture (EDC) systems and rigorous data governance frameworks. These methods are crucial in managing the increasing volume and complexity of healthcare data. The study's findings reveal that big data analytics and artificial intelligence (AI) are pivotal in transforming healthcare data management, enabling personalized and precise patient care. However, these technologies also introduce challenges related to data security, interoperability, and ethical considerations, particularly concerning patient privacy and the transparency of AI-driven decision-making processes. In conclusion, the study emphasizes the need for continuous innovation and improvement in healthcare data management practices. Recommendations include the integration of blockchain technology, cloud computing, and real-time data from wearable devices to address existing challenges and enhance data management capabilities. The study advocates for a balanced approach that prioritizes patient privacy, ethical governance, and regulatory compliance to ensure that technological advancements contribute positively to patient outcomes and healthcare efficiency.

Privacy-Preserving Electric Vehicle Charging Recommendation by Incorporating Full Homomorphic Encryption and Secure Multi-Party Computing

Electric vehicle (EV) charging recommendation can significantly improve global planning performance, corresponding to an increasing risk of privacy leakage. Based on this, this paper investigates the privacy data preservation strategy during the interaction between EVs and charging facilities. It proposes a privacy preservation strategy that aims to ensure EV information security. In a cloud computing environment, users do not want other users and cloud providers to have access to their personal information, which is precisely the problem that secure multi-party computing (SMPC) can solve. At present, full homomorphic encryption (FHE) can solve the problem of user data privacy preservation in cloud computing and big data environments and can realize the whole encryption process. Therefore, a more reasonable charging station selection scheme is provided under the computation of privacy preservation strategies incorporating the FHE-SMPC method. The effectiveness and implementation feasibility of the designed privacy preservation strategy in practical applications is verified through testing and comparative analysis. The results show that the developed strategy can significantly reduce the risk of privacy leakage with limited communication resources and computation time consumption. The results provide new perspectives and methodologies for interactive privacy preservation between EVs and charging stations, with application potential.

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

Transforming Clinical Research: The Power of High-Throughput Omics Integration.

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

A Study the review of Duplicate Data in Cloud Computing

In the realm of cloud computing, the management of data redundancy, often referred to as duplicate data, poses a significant challenge due to its implications on storage efficiency, data integrity, and overall system performance. This study provides a comprehensive review of the current strategies employed to detect, manage, and eliminate duplicate data in cloud environments. Duplicate and nearly duplicate web pages cause major issues for web search engines. Duplicate document identification, or the process of finding document pairs that represent the same entity, is a basic component of data cleansing. In order to address the structural heterogeneity issue, data must first undergo a data preparation step that involves parsing, data transformation, and data standardization. The findings aim to aid cloud service providers and users in understanding the importance of effective data deduplication strategies to optimize storage resources and enhance cloud computing performance.

Bibliometric Analysis on the Application of IoT in Smart Manufacturing System in Industry 4.0

The rapid integration of Internet of Things (IoT) technologies in smart manufacturing systems has been a pivotal development within the framework of Industry 4.0. This study conducts a comprehensive bibliometric analysis to examine the evolution, current trends, and emerging research areas related to IoT applications in smart manufacturing. By analyzing data from a decade of scholarly publications, this research identifies key themes, influential authors, and collaborative networks that have shaped the field. The findings reveal that foundational technologies such as the internet, cloud computing, and big data are central to the development of smart manufacturing, while newer innovations like digital twins and blockchain are gaining prominence. The study also emphasizes the importance of strategic collaboration among researchers and industry professionals in advancing IoT adoption. The practical implications suggest that manufacturers should prioritize both foundational digital infrastructure and emerging IoT applications to enhance efficiency, security, and sustainability. This analysis provides a roadmap for future research and practical implementation, highlighting the critical areas where IoT can further revolutionize smart manufacturing systems.

Application of High-Frequency Intelligent Sensing Network in Monitoring and Early Warning of Water Quality Dynamic Change

By implementing a high-frequency intelligent network of sensors, this work explores continuous monitoring and alerting for dynamic changes in water quality. Life depends on water, yet pollution is a greater menace. For this reason, precautions and careful observation are necessary. Typically, the focus on conventional water quality system monitoring is too much on data collection and needs more on analysis and extraction, limiting its capacity to offer thorough solutions. Making informed decisions becomes more complicated when there are discrepancies like damaged data, loss from power outages, or transmission issues. The proposed High-Frequency Intelligent Sensing Network (HFISN) monitoring system uses cloud computing, IoT and Big data technologies for intelligent sensing. Researchers developed it to address various challenges. Researchers recommend Nephelometric Turbidity Unit (NTU) Sensor installation to enhance the system’s performance and facilitate better monitoring of sedimentation, particle issues, and water purity. This sensor makes it possible to make more informed decisions by expanding the platform’s dataset. The solution not only resolves data cleaning and analysis issues but also includes intelligent early-warning capabilities for timely alerts. Quantum Cloud (QC) technology is employed to enhance security and accessibility. Test findings confirm its robustness with extra features and a built-in turbidity sensor. Because the platform ensures data accuracy and dependability, it provides decision-makers with a solid foundation to protect water resources.

Research on Student Management Platform Based on Big Data Under Low-Carbon Environment

This paper makes a comprehensive investigation on the development and implementation of low-carbon student management platform in educational institutions. The platform adopts advanced information technology, including cloud computing and big data analysis, aiming at solving urgent environmental problems by analyzing the dynamic learning data and daily behavior data of college students. The implementation of the platform has greatly reduced carbon emissions, especially greenhouse effect variables and dormitory electricity consumption. The observed impact highlights the effectiveness of using academic management platform to promote carbon emission reduction. However, this study acknowledges the existing limitations and predicts the long-term challenges, and emphasizes the need for continuous innovation and research in the intersection of artificial intelligence and environmental sustainable development. Generally speaking, the development and deployment of low-carbon student management platform is a key step to promote the sustainable development of educational environment.

Research analytics capabilities (RAC) survey: development, validation and revision using the Rasch model

PurposeThe research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.Design/methodology/approachAs businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.FindingsAfter revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.Originality/valueVery limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

Scientific Integration of Operations Research and Machine Learning for Data Centre Optimization

In this study, we explore how to optimize data center operations by combining Operations Research (OR) and Machine Learning (ML) methodologies with Python-based categorization algorithms. Using Scikit-learn and TensorFlow, two Python libraries, we investigate how ML algorithms might be integrated with OR techniques like queuing theory and linear programming to forecast workloads and allocate resources more effectively. Problems with scheduling workloads, allocating resources, and managing energy consumption are at the heart of our research into data center optimization. The goal of this comprehensive framework is to create more effective and environmentally friendly data centre operations by systematically evaluating Python-based categorization models in response to changing workload demands and environmental circumstances. Introduction: The backbone of our digital infrastructure, data centers stand tall in the ever-changing world of contemporary technology. A vast variety of online services, including social media platforms, e-commerce websites, cloud computing, and big data analytics, rely on the servers, storage devices, networking gear, and other essential components housed in these expansive facilities. Meeting the ever-increasing demands for computational resources while simultaneously enhancing performance, efficiency, and cost-effectiveness is a daunting task for data centers, which are already struggling to keep up with the exponential growth in both the amount and complexity of digital data. Objectives: Our goal in writing this article is to delve into the ways in which data center optimization intersects with Operations Research and Machine Learning. Data center optimization presents a wide range of problems, and this course will explore the theory, methods, and best practices for using OR and ML to solve these problems. To develop an integrated framework that combines operations research (OR) and machine learning (ML) techniques to optimize the performance, energy efficiency, and reliability of data centers. Methods: Optimization strategies that improve data center operations in terms of performance, efficiency, and sustainability. These proposed strategies make use of both OL and ML techniques. Data center operators can optimize resource allocation, workload management, temperature control, energy consumption, and anomaly detection in real-time by formally stating the optimization problem in a mathematical framework. This allows for informed decision-making, systematic analysis of trade-offs, and the implementation of adaptive control strategies. Results: The visualization depicts the projected energy usage in terms of bandwidth for both approaches, compared to the actual values. In general, although both methods demonstrate potential, additional refinement and optimization may be necessary to attain superior outcomes in real-life situations. This discussion presents an analysis of the performance of both procedures and offers insights into their respective strengths and shortcomings, which can serve as a foundation for further investigation or improvement of the approaches. Conclusions: By using a comprehensive and multidisciplinary approach, we can optimize data centres in a way that boosts efficiency and performance while simultaneously encouraging innovation, resilience, and sustainability in data centre operations. Also, Data center gains in resource operators can achieve considerable utilization, energy efficiency, and overall system performance by integrating optimization algorithms, predictive analytics, and adaptive control strategies

Deduplication on Encrypted Data in Cloud Computing

Cloud storage is a crucial component of cloud computing, allowing users to expand their storage without upgrading their equipment and overcome resource constraints. Cloud users' data is always encrypted before being outsourced to ensure their security and privacy. However, encrypted data may result in a significant waste of cloud resources. Storage complicates data sharing among authorized users. We continue to face issues with encrypted data storage and deduplication. Traditional deduplication strategies are designed for certain application settings where data owners or cloud servers have full control over the process. They cannot meet data owners' varying requests based on data sensitivity. This study proposes a flexible data storage management method that combines deduplication and access control across various Clouds. Service Providers (CSP). We assess its performance through security analyses, comparisons, and implementations. The results demonstrate security, effectiveness, and efficiency for actual applications.

International Law Protection of Cross-Border Transfers of Personal Information Based on Cloud Computing and Big Data

Cross-border data is crucial for the digital economy s growth, but uncontrolled transfers risk national security, public interests, corporate interests, and data sovereignty. Currently, cross-border data regulation lacks unified global rules, with existing ones dominated by Europe and the U.S. China needs to improve its international legal protection for cross-border personal information transfer. This paper examines China s mechanisms for personal data privacy protection in digital trade, including frameworks for big data, cloud computing, and cloud service selection for data lifecycle privacy protection. It suggests clarifying data controllers and exporters obligations, strengthening regulatory responsibilities, improving cross-border data legislation, enhancing law operability, and advocating for stronger international cooperation and participation in global rule-making to protect personal privacy information rights.