Role Of Big Data Research Articles

Data utilisation among employees in SMEs in Destination Limfjorden, Denmark

ABSTRACT This article explores the efforts of Destination Limfjorden in Denmark to enhance employees’ competencies and skills in small and medium-sized tourism enterprises (SMEs) through the strategic use of data. The study employs a mixed-method approach, integrating digital questionnaires and semi-structured interviews to identify the competence needs of stakeholders. The research reveals a significant need for improved digital guest services and highlights the importance of data usage within companies, network building and general competence needs. The article also reviews the literature on the role of big data in augmenting tourism research and the challenges SMEs face in harnessing big data analytics. It underscores the need for robust knowledge management initiatives, targeted skill development programmes and a culture of collaboration among tourism SMEs. The article concludes by emphasising the increasing significance of big data competencies for SMEs in the tourism sector, particularly in remote European regions, and the potential of data analytics in fostering resilience among SMEs and enhancing their competitiveness within the evolving dynamics of the tourism industry landscape.

The role of big data in the secondary market

With the rapid development of information technology, Big Data has emerged as one of the important characteristics of the contemporary era. In the secondary market, the application of Big Data is increasingly widespread, which has a profound impact on market structure, trading patterns, and investment decisions. This paper aims to explore the utilization of Big Data in the secondary market, including data analysis techniques, enhancement of decision efficiency, augmentation of market transparency, and innovation in risk management practices while discussing the drawbacks of using Big Data. Then, the study focuses on how to improve and enhance the application of big data in the secondary market in the future. The findings underscore that Big Data plays an instrumental role in shaping the dynamics of the secondary market, this is predominantly manifested in the prediction of stock market trends, the assessment of investment risks, and so on. However, certain challenges persist. Continual development and improvement of Big Data can facilitate more informed investment decision-making.

The Impact of Public Health on Health Monitoring

The relationship between public health and health monitoring has evolved significantly, shaped by historical developments, technological advancements, and changing societal needs. This paper examines the mutual influence of public health initiatives and health-monitoring systems, particularly in the context of late 20th-century England. The historical perspective highlights the role of public health efforts in establishing systematic health monitoring, which was critical for addressing widespread health issues. Key public health concepts and their implications for health monitoring are discussed, followed by an exploration of modern technological advancements, such as mobile health applications and wearable devices, which have transformed personal and population health management. Finally, the paper considers challenges and future directions, focusing on ethical concerns, the role of big data, and the need for integrated global health strategies. The paper argues that public health and health monitoring are increasingly interdependent, with advancements in health monitoring playing a vital role in shaping the future of public health. Keywords: Public health, Health monitoring, Technological advancements, Health surveillance, Wearable devices.

The role of big data and AI in enhancing biodiversity conservation and resource management in the USA

The rapid advancement of big data and artificial intelligence (AI) technologies has opened new frontiers in biodiversity conservation and resource management, particularly in the USA. This review paper explores the significant roles these technologies play in enhancing conservation efforts and resource management strategies. The paper begins by contextualizing the importance of biodiversity in the USA and the existing challenges in conservation practices. It then delves into the realm of big data, discussing its sources, collection methods, storage, integration, and real-world applications. Furthermore, it examines AI technologies, highlighting their capabilities in data analysis, predictive modeling, and habitat monitoring. The synergy between big data and AI is also analyzed, showcasing how their integration can lead to more informed decision-making and efficient management practices. Case studies illustrate the practical applications and benefits of these technologies. The paper concludes with a summary of key findings, recommendations for policymakers and practitioners, and suggestions for future research directions. Through this comprehensive review, the paper underscores the transformative potential of big data and AI in safeguarding biodiversity and managing natural resources effectively.

High seas in the cloud: the role of big data and artificial intelligence in support of high seas governance – The Sargasso Sea pilot

High seas in the cloud: the role of big data and artificial intelligence in support of high seas governance – The Sargasso Sea pilot

Solving supply chain management issues with AI and Big Data analytics for future operational efficiency

This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.

Digitization of logistics and supply chain management

The article presents theoretical and applied aspects of digitalization of logistics and supply chain management. It is emphasized that the processes of digitalization of supply chains and logistics are a modern requirement for increasing business efficiency, as well as solving environmental, social and management problems of successful companies. Digitization of supply chains and logistics is defined as the integration of digital technologies and the implementation of digital tools to optimize, automate and increase the efficiency of all stages of the logistics process. Key modern technologies in logistics and supply chains are examined, including artificial intelligence (AI), the Internet of Things (IoT) and Big Data. The role of AI in optimizing logistics processes, forecasting market behavior, inventory management and route planning is outlined. Emphasis is placed on the importance of IoT for tracking goods, monitoring storage conditions and vehicle maintenance. The role of Big Data in optimizing supply chains, improving decision-making and compliance with regulatory requirements is analyzed. The use of digital documentation and electronic document circulation are highlighted as important areas of digitization. The difference between digitization of documents and digitalization of document circulation is revealed: digitization, or digital preservation, refers to the transfer of information from paper documents to electronic format for convenient and safe storage of files and access to them in the future; digitalization involves the creation of documentation in computer applications, its intelligent processing and analysis, its accumulation in digital repositories, and the wide use of its data to solve various problems. It is noted that one of the main directions of digitization of documents is the automated reading of documents and data entry, which is implemented using specialized software for automatically extracting information from paper documents and entering it into digital systems. The list of the main logistics documents that are subject to digitization and the digitization of which leads to an increase in the efficiency of the supply chain (delivery confirmation, bill of lading, packing lists, invoices, cargo manifest, etc.) is provided. It was concluded that digitalization of logistics is a significant factor for modern companies seeking to increase their competitiveness in the market. It also enables the integration of all company divisions, improves product tracking, speeds up customs procedures, optimizes transport costs and increases the flexibility of return management. Despite the shortcomings that accompany this process, digitalization of logistics is necessary to achieve sustainable development and effective management of supply chains in today's world.

Role of big data and technological advancements in monitoring and development of smart cities

Driven by rapid advancements in technology and data science, a revolutionary transformation is sweeping across environmentally friendly cities worldwide. This surge stems from a pressing need to tackle the intricate complexities of urban sustainability, encompassing everything from infrastructure and governance to fragmented design and technological solutions. To effectively manage these complexities and accurately measure, assess, and optimize their sustainability performance, sustainable communities are increasingly tapping into the potential of smart city technologies, particularly big data and its fictionalized applications. This trend culminates in the emergence of smart cities. This article delves into the current state of research surrounding data-driven, environmentally conscious smart cities, aiming to assess the extent to which these two concepts are currently being integrated and identify potential gaps in this field. Through a strong emphasis on evidence-based research, the study underscores the potential of big data technologies to offer innovative approaches for monitoring, comprehending, evaluating, and ultimately managing sustainable urban development. It further highlights the crucial role of data-driven advancements in formulating strategic development policies and operational management procedures, ensuring that environmentally conscious cities can continue to contribute to sustainability goals even amidst rapid urbanization.

The role of big data in shaping the agribusiness industry

The role of big data in shaping the agribusiness industry

Impact of green innovation on carbon reduction in China

Green innovation directly encompasses the two major concepts of green and innovation in the new development concepts, which provides a powerful driving force to support Chinese-style modernisation. This paper empirically tests the relationship between green innovation and carbon emission intensity using a double fixed effects model. Based on the panel data of 30 provinces in China, the mediation effect model of “green innovation-big data-carbon emission” is constructed. The result shows that green innovation has a noticeable direct negative effect on urban carbon emission intensity. The conclusions are robust after considering measurement errors and endogenous problems. Furthermore, it is found that big data plays a significant role in strengthening the relationship between green innovation and carbon emission intensity. The findings in this study not only advance the study on green innovation and carbon emissions but also provide a new perspective on the role of big data.

Big data, green loans and energy efficiency

Green digital finance is an instrumental way to promote technological innovation, accelerate the low-carbon transition, and foster sustainable development. With the emergence of green digital finance, how does it affect firms’ energy use efficiency? Using big data and green loans as an entry point, the impact of green digital finance on corporate energy efficiency and the role of big data are examined. We provided a simple theoretical model to analyze the green loaning behavior of the banking sector after applying big data and its impact on corporate energy efficiency. Our research finds that: (1) The application of big data can make it easier for the banking sector to obtain loan companies’ information and reduce loan delinquency rates. This will reduce the information and transaction costs of the banking sector and expand the scale of optimal green loans. (2) The optimal green loan scale has a negative relationship with the optimal green loan interest rate. (3) The application of green loans by firms can improve energy efficiency and have a range of impacts on firms’ decision-making, including an increase in the emission reduction ratio, innovation probability, output, and profit, followed by a decrease in energy consumption and pollution emissions. This paper further clarifies the channels through which green digital finance affects energy efficiency and specifies the role of big data in green digital finance. This could help relevant policymakers design more effective green digital finance policies, contributing to carbon peaking and carbon neutrality goals.

The role of big data in detecting and preventing financial fraud in digital transactions

The role of big data in detecting and preventing financial fraud in digital transactions

The role of big data and IoT in logistics supply chain management of e-commerce

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.

Big Data in Tourism Destinations: A Systematic Literature Review

Tourism is an industrial sector that has a variety of data originating from tourists. This data can be utilized and reprocessed through the application of artificial intelligence technology such as big data and machine learning to analyze and predict tourist patterns so that it can be used in developing the tourism sector. This research aims to present a systematic literature review regarding the role of big data and machine learning in the tourism context. This research reviews 25 research papers related to big data and machine learning applied in the tourism industry. The categorization of tourism research related to big data and machine learning is based on research published from 2018 to 2023. The focus of this research is to provide an in-depth review based on journal rankings, research objectives, types of data used, and algorithms applied in the research . The results of this research are to provide a systematic literature review that can be used to help future researchers discover new research topics and present insights into future prospects regarding the use of big data and machine learning in tourism.

Analysis of Current Situation, Problems and Countermeasures of Big Data in the Field of Engineering Management

Currently, the field of engineering management is experiencing rapid development and has become a significant force in promoting economic and social growth. Due to its complexity, creativity, systematization, and risk characteristics, the field of engineering management presents individuals with significant technical needs and processing difficulties. Big data has achieved significant advancements in the engineering management field due to its characteristics, including large data volumes, various types, and predictability. By utilizing big data, a sizable amount of engineering data can be rapidly analyzed and optimized for design solutions, resulting in improved efficiency and increased accuracy in decision-making while also reducing costs. However, the role of big data in engineering management is limited by issues such as data insecurity, low quality, and a shortage of professionals. This paper proposes solutions to address these challenges, including strengthening data security, enhancing data quality control, and improving personnel training and technological innovation.

The role of big data in healthcare: A review of implications for patient outcomes and treatment personalization

The integration of big data analytics in healthcare has ushered in a transformative era, redefining the landscape of patient care and treatment strategies. This review examines the multifaceted implications of big data on patient outcomes and the individualization of medical interventions. Delving into the foundational elements of big data, we explore its evolution in the healthcare sector and highlight pivotal applications, such as predictive analytics, treatment personalization, and population health management. The paper underscores how big data-driven insights have revolutionized diagnosis and early detection, leading to more accurate and timely interventions. Treatment planning has witnessed a paradigm shift, with the tailoring of therapeutic approaches based on robust data analyses, fostering the realization of personalized medicine. Moreover, the role of big data in enhancing patient engagement and empowerment is explored, illuminating the potential for collaborative and informed decision-making. Despite these advancements, ethical considerations and challenges loom large. Privacy concerns, data security, and the ethical use of patient information demand meticulous attention to ensure the responsible application of big data in healthcare. The paper discusses the evolving regulatory frameworks and strategies to address these pressing issues. Looking ahead, the review outlines emerging trends and technologies poised to shape the future of big data in healthcare. It identifies research opportunities and encourages interdisciplinary collaborations to further propel innovation in this dynamic field. By addressing challenges and envisioning future possibilities, it seeks to contribute to the ongoing dialogue surrounding the responsible and impactful integration of big data in shaping the future of healthcare.

Systematic Literature Review on the Role of Big Data in IoT Security

The Internet of Things (IoT) is an interconnected system of physical objects that are embedded with different sensors (for receiving information), chips, software, and other techniques, which allow connecting and transferring of data to other devices via the Internet without human involvement. Since the number of IoT devices is increasing, large amounts of data are being generated from different sources in different formats. This information needs to be used effectively to gain useful insights for enhancing IoT security. Hence, big data techniques are proposed for managing the data to overcome different issues of IoT. Despite the outstanding achievements in IoT security, a systematic literature review (SLR) on implementing big data for securing IoT is lacking. The number of existing related SLRs is limited. Hence, this paper provides a systematic literature review on the use of big data for securing IoT devices. It summarizes the relevant literature produced during the last six years and provides meaningful insights gained by these existing studies. Moreover, it provides a discussion on the sources of IoT big data, the techniques and approaches of big data for securing IoT systems. Current challenges and future research directions are discussed.

The role of big data in the integrated development of culture and tourism public services

The integrated development of culture and tourism public services can better enhance the value of scenic spots, promote the spread of Chinese excellent culture, and drive the development of tourism economy. Big data technology can predict the future development law of transactions based on historical data. It can meet the needs of tourists and tourism enterprises in a targeted manner. This paper aims at the integrated development practice of tea culture and tourism public services in the era of big data, analyzes the influencing factors of the integrated development of tea culture tourism and public services in Tianfu Tea Garden and the problems existing in the development process of Tianfu Tea Garden, and designs data analysis algorithms through big data technology, and put forward development suggestions combined with big data technology, mainly including improving the industrial chain; improving the overall development scope of the scenic spot; strengthening the quality training of tea garden employees; improving infrastructure; highlighting the connotation of tea culture in scenic spots; developing and researching new tourism products; increase the publicity and construction of scenic spots, so as to provide reference for the development of tea culture and tourism in Tianfu Tea Garden.

The Role of Big Data in Circular Supply Chain Practices and Corporate Sustainability Performance

The green industry is a national priority target, with one of the indicators being the application of a circular economy. The application of a circular economy to the entire supply chain process is an effort to extend the service life of materials so that sustainability arises. The application of circular supply chains needs to be supported by digital technology to improve processes. This study aims to analyze the effect of circular supply chain practices on corporate sustainability performance and the moderating effect of Big Data. The hypothesis testing using SEM analysis tools. The number of samples used was 140, who are currently working or have worked in companies that have implemented a circular economy. [KM1] The results of the study show that Circular Supply Chain Practices do not influence on Corporate Sustainability Performance, so the moderating effect of Big Data is not supported. Therefore, managerial parties need to implement a circular supply chain in the company's business processes. Managers need to provide technical training related to designing environmentally friendly products and educational strategies to customers regarding the application of a circular economy. Nonetheless, this study also has limitations, in that it consists of only one variable, Circular Supply Chain practices, which affect Corporate Sustainability Performance. Thus, the next researcher should add other variables that can affect Corporate Sustainability Performance, such as the purchasing realized absorptive capacity variable. [KM1]Refer in research method section for my comment.

Net Zero Dairy Farming—Advancing Climate Goals with Big Data and Artificial Intelligence

This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts.