Big Data Applications Research Articles

How advanced is your digital public health system? A qualitative analysis of suitable indicators

Abstract Background Revealing the full potential of digital public health (DiPH) systems necessitates a wide-ranging tool to assess their maturity. Essential domains that need to be considered include the literacy and interest in DiPH tool application by society and the workforce and the legal and ICT maturity of DiPH systems. No review has investigated indicators on these national DiPH system maturity (DiPHSM) domains yet. Our narrative review and qualitative analysis aimed to map the landscape of indicators related to DiPHSM measurement and rank them based on their importance for such assessments. Methods As original indicators were not published in scientific databases but as grey literature, we used DuckDuckGo to apply a pre-defined search strategy for 11 countries from all continents classified as having reached level 4 of 5 in the Global Digital Health Monitor. Of the 1484 identified references, 137 were included which named 15806 indicators. Consensus on importance was defined as at least 3 or 4 authors rating an indicator as important. Results We recognized 180 indicators on different constructs with importance for DiPHSM analysis, including the availability and use of Smartphones, computers, the Internet or the DiPH intervention, the existence of skilled workforce, infrastructure (investment), and interoperability between interventions, the secondary use of health data, a DiPH strategy and controlling agency, or the application of big data and artificial intelligence for health data collection, analysis, and sharing. Conclusions Our study holds the potential to develop more comprehensive tools for DiPHSM assessments. Further examination is required to analyze the suitability and applicability of all identified indicators for diverse healthcare settings. By working towards a uniform evaluation of DiPHSM, we foster informed decision-making among healthcare planners and practitioners, improve resource distribution, and continue to drive innovation in healthcare delivery. Key messages • Maturity assessment tools need to consider the complexity of DiPH systems. Thereby, DiPH system evaluations need to be accompanied by analyses of the legal, ICT, and literacy perspective maturities. • New methods are needed to systematically assess and use multidisciplinary grey literature for research.

Research Trends and Hotspots of Big Data in Ophthalmology: A Bibliometric Analysis and Visualization

ABSTRACT Purpose The burst of modern information has significantly promoted the development of global medicine into a new era of big data healthcare. Ophthalmology is one of the most prominent medical specialties driven by big data analytics. This study aimed to describe the development status and research hotspots of big data in ophthalmology. Methods English articles and reviews related to big data in ophthalmology published from January 1, 1999, to April 30, 2024, were retrieved from the Web of Science Core Collection. The relevant information was analyzed and visualized using VOSviewer and CiteSpace software. Results A total of 406 qualified documents were included in the analysis. The annual number of publications on big data in ophthalmology reached a rapidly increasing stage since 2019. The United States (n = 147) led in the number of publications, followed by India (n = 77) and China (n = 69). The L.V. Prasad Eye Institute in India was the most productive institution (n = 50), and Anthony Vipin Das was the most influential author with the most relevant literature (n = 45). The electronic medical records were the primary source of ophthalmic big data, and artificial intelligence served as the principal analytics tool. Diabetic retinopathy, glaucoma, and myopia are currently the main topics of interest in this field. Conclusions The application of big data in ophthalmology has experienced rapid growth in recent years. Big data is expected to play an increasingly significant role in shaping the future of research and clinical practice in ophthalmology.

A Comparative Analysis of Machine Learning Algorithms for Big Data Applications in Predictive Analytics

As the volume and complexity of data continue to grow, predictive analytics has emerged as a vital tool for extracting actionable insights from big data, driving decision-making across various domains such as healthcare, finance, and e-commerce. However, selecting an appropriate machine learning algorithm for predictive analytics applications is challenging due to differences in algorithmic performance, computational requirements, and scalability, especially in the context of big data. This paper provides a comprehensive comparative analysis of popular machine learning algorithms utilized in predictive analytics, specifically focusing on their effectiveness and feasibility in big data environments. The study categorizes algorithms based on learning types—supervised, unsupervised, and reinforcement learning—and evaluates their performance across multiple dimensions: prediction accuracy, computational efficiency, scalability, and suitability for real-time analytics. Through a detailed analysis of algorithms, including linear regression, decision trees, support vector machines, neural networks, and clustering techniques, we assess each method’s strengths and limitations in handling large datasets. Additionally, the study introduces a series of metrics, such as accuracy, F1-score, and training time, as benchmarks for assessing the algorithms’ predictive capabilities and computational viability. A hypothetical case study demonstrates the application of these algorithms on a sample big data set, providing insights into their real-world performance across different predictive analytics scenarios. Visual data representations, including comparative tables and performance graphs, offer a clearer perspective on the trade-offs among algorithm choices. The findings highlight that while certain algorithms like random forests and neural networks achieve higher accuracy in prediction tasks, they may also require substantial computational resources, posing limitations for real-time processing in big data applications. This paper concludes with recommendations for selecting machine learning algorithms based on specific predictive analytics objectives, data characteristics, and processing requirements. Furthermore, it discusses the challenges associated with implementing these algorithms in big data contexts and explores potential advancements, such as the integration of deep learning and the use of distributed computing, as promising directions for enhancing predictive analytics performance in future applications.

Applications of Big Data and Blockchain Technology in Food Testing and Their Exploration on Educational Reform

This study reviews the applications of big data (BD) and blockchain technology in modern food testing and explores their impact on educational reform. The first part highlights the critical role of BD in ensuring food safety across the supply chain, discussing various data collection methods, such as national and international food safety databases, while addressing the challenges related to data storage and real-time information retrieval. Additionally, blockchain technology has been explored for its ability to enhance transparency, traceability, and security in the food-testing process by creating immutable records of testing data, ensuring data integrity, and reducing the risk of tampering or fraud. The second part focuses on the influence of BD and blockchain on educational reform, particularly within food science curricula. BD enables data-driven curriculum design, supporting personalized learning and more effective educational outcomes, while blockchain ensures transparency in course management and credentials. This study advocates integrating these technologies into curriculum reform to enhance both the efficiency and quality of education.

The Impact of the Digital Economy on China's Economic Development

Abstract: This paper explores the impact of the digital economy on China's economic development. The digital economy, as a new form of the economy following the agricultural and industrial economies, centres on data resources and modern information networks, driving the digital transformation of all factors of production. The Chinese government places great importance on the development of the digital economy, clarifying its direction and key tasks through policy documents such as the "14th Five-Year Plan for Digital Economic Development". This paper analyses the current state of development of the digital economy in e-commerce, digital payments, and big data applications, and discusses its impact on industrial structure, employment, and consumption patterns. The study shows that the digital economy has promoted the upgrading of traditional industries and the growth of emerging industries, while also bringing about changes in employment structure and innovation in consumption methods. However, it also poses challenges to the job market, which need to be addressed through skills training and educational reform.

RNEP: Random Node Entropy Pairing for Efficient Decentralized Training with Non-IID Local Data

The proliferation of edge devices and advancements in Internet of Things technology have created a vast array of distributed data sources, necessitating machine learning models that can operate closer to the point of data generation. Traditional centralized machine learning approaches often struggle with real-time big data applications, such as climate prediction and traffic simulation, owing to high communication costs. In this study, we introduce random node entropy pairing, a novel distributed learning method for artificial neural networks tailored to distributed computing environments. This method reduces communication overhead and addresses data imbalance by enabling nodes to exchange only the weights of their local models with randomly selected peers during each communication round, rather than sharing the entire dataset. Our findings indicate that this approach significantly reduces communication costs while maintaining accuracy, even when learning from non-IID local data. Furthermore, we explored additional learning strategies that enhance system accuracy by leveraging the characteristics of this method. The results demonstrate that random node entropy pairing is an effective and efficient solution for distributed learning in environments where communication costs and data distribution present significant challenges.

Impact of smart factory adoption on manufacturing performance and sustainability: an empirical analysis

PurposePrevious studies have focused on explaining the developmental paths and the relevant skills necessary for smart factories, based on an extensive review of the literature. Unfortunately, there is a deficit of empirical analyses that present an in-depth overview of smart factory development. Although the literature supports the benefits of smart factories, it remains unclear whether there should be government intervention (GI) to facilitate or regulate such adoption. This study will provide an in-depth empirical analysis of smart factory adoption (SFA) and its role in manufacturing performance (MP) and sustainable manufacturing (SM).Design/methodology/approachThis study used non-probability convenience and referral sampling techniques for data collection. This approach considered production managers from each company that participated in the survey questionnaire; thus, each production manager represented one firm. A total of 240 managers from several manufacturing companies participated in the study. This study employed direct and moderating hypotheses tested using PROCESS Macro, which Andrew Hayes developed for SPSS.FindingsThe study identified three fundamental elements of a smart factory: manufacturing big data (MBD), process automation (PA) and supply chain integration (SCI) and analyzed them individually to see how they affect MP. According to the results, building a smart factory has positive and significant impacts on MP and SM. Furthermore, this study explores the role of GI in promoting smart factory deployment for both production performance and sustainable production. The study found that GI did not have a significant moderating effect but did have a positive relationship with SM.Research limitations/implicationsThis study contributes to the literature on smart factories by examining the impact of SFA on MP and SM. This provides a more comprehensive overview of the potential benefits of smart factories across various aspects, such as the application of big data, adoption of automation technology and integration of the supply chain. This study suggests that managers and decision-makers consider investing in smart factory implementation to improve factory productivity and enhance sustainability. Policymakers and government officials can promote the adoption of smart factories by providing incentives, funding, and resources to manufacturing firms.Originality/valueThere is a scarcity of research measuring the actual performance of manufacturing firms that have already adopted smart factories, and this study seeks to address this gap in the literature. This study focuses on the implementation of manufacturing big data, process automation and supply chain integration and how the adoption of these technologies improves MP and provides a SM environment by conducting a real-time study of manufacturing organizations. This study presents an initial effort to explore the role of government involvement in promoting smart factories.

Research on AIS Data Analysis and Its Integration into Discipline-specific Teaching

The processing of ship AIS (Automatic Identification System) data plays a crucial role in the digital transformation of the shipping industry. From data collection to data storage, data analysis, and data visualization, AIS data processing technology provides robust support for the shipping sector. In terms of data collection, the platform utilizes distributed message queues to aggregate AIS data. For data storage, the platform efficiently manages and stores massive amounts of AIS data using data warehouses. When it comes to data analysis, the platform mines and analyzes AIS data through Lambda, extracting valuable information such as ship trajectories and sailing speeds, thereby supporting areas such as ship management, navigation safety, and marine research. In data visualization, the platform intuitively displays ship navigation status through charts and maps, providing strong support for decision-making. As an essential component of big data applications, AIS data analysis technology is playing an increasingly important role in the shipping industry. The big data technology major of Jiangsu Maritime Institute integrates big data applications like AIS data analysis into its curriculum, taking the upgrading needs of the shipping industry as an entry point. It actively plans the cultivation of composite big data skilled talents under the backdrop of smart shipping, comprehensively serving the upgrading of the shipping industry and promoting the transformation from a major shipping country to a shipping powerhouse.

The concepts of sustainability value and the application of big data in enhancing the ability of sustainability marketing

PurposeWe seek to contribute to the literature by comparing and analyzing the relationship between Australian and Taiwanese students regarding environmental value attitude, action intention and green marketing intention. Specifically, by comparing the green marketing intention of hospitality and tourism (H&T) students in the East with that in the West.Design/methodology/approachA well-designed curriculum examines student thinking and behavior (learn). This study compares and analyzes the value and attitude and the application of big data to green marketing among Taiwanese and Australian university students using the stimulus-organism-response (S-O-R) model. Structural equation modeling (SEM) was used to test the hypotheses in a sample of 633 H&T students in Taiwan (389) and Australia (244).FindingsThis study also shows how the national differences between Australia and Taiwan have interference effects on the relationship between value attitudes and action intentions and between action intentions and the green marketing intention. We also combined the application of big data and related variables and estimated the mediating effect of related variables to evaluate the impact on action intentions and green marketing of big data applications.Practical implicationsThere are significant differences in the sustainable behavior and intentions of H&T higher education students that reflect the educational differences between the East and the West. These different results may be due to a lack of natural resources and the relatively smaller size of Taiwan. With the strengthening of environmental action intention (AI) and green marketing intention (GMI), Taiwanese hospitality and management (H&M) students' sense of crisis increases, and their performance in GMI is slightly higher than that in Australia.Originality/valueThese findings indicate that it is necessary to consider students' ecological concepts, environmental knowledge, environmental value attitude and environmental action intention to improve their intentions to engage in green marketing under the stimulus-organism-response (S-O-R) framework. We also found that environmental knowledge has a mediating effect on the relationship between ecological concepts and environmental value attitude.

The Influence of Social Media on College Choice among Undergraduates Majoring in Big Data Management and Application in China: Taking Guilin University of Electronic Technology as an Example

This study aims to investigate the influence of social media on college choice among undergraduates majoring in Big Data Management and Application in China. The study attempts to reveal how information on social media platforms such as Weibo, WeChat, and Zhihu influences the cognition and choice process of prospective students. By employing an online quantitative survey questionnaire, data were collected from the 2022 and 2023 classes of new students majoring in Big Data Management and Application at Guilin University of Electronic Technology. The aim was to evaluate the role of social media in their college choice process and understand the features and information that most attract prospective students. Social media has become a key factor influencing the college choice decision-making of undergraduates majoring in Big Data Management and Application in China. Students tend to obtain school information through social media platforms and use this information as an important reference in their decision-making process. Higher education institutions should strengthen their social media information dissemination, providing accurate, timely, and attractive information. It is also necessary to ensure effective management of social media platforms, maintain a positive reputation for the school on social media, and increase the interest and trust of prospective students. Simultaneously, educational decision-makers should consider incorporating social media analysis into their recruitment strategies to better attract new student enrollment. This study provides a new perspective for understanding higher education choice behavior in the digital age, particularly by revealing the importance of social media in the educational decision-making process. This has important practical and theoretical implications for higher education institutions, policymakers, and social media platform operators.

Research on Hybrid Teaching Driven by Big Data Application Development Competition

With the increasing emphasis placed by the country on vocational education, it has become a key focus for vocational colleges to encourage their students to participate in national vocational skills competitions, through which they can enhance their professional qualities, theoretical knowledge, practical skills, and employability. The big data technology major of Jiangsu Maritime Institute has analyzed the knowledge and skill points required in big data application development skills competitions, and has constructed a project-driven hybrid teaching approach based on the Chaoxing Cloud Course Platform, which utilizes a SPOC (Small Private Online Course) approach. By aligning with the workflows and tasks of big data technology-related job positions, the major integrates skills competition projects into its curriculum, realizing a learner-centered, integrated online and offline learning approach. Through teaching practice, this teaching method has achieved good results, improving the quality of teaching and students' learning enthusiasm, and providing new methods and perspectives for the reform of big data technology professional courses in higher vocational education.

Expert consensus on the application of big data on rehabilitation medicine (2024 edition)

With the development and progress of technology, the transformation of biomedical models, and the application of digital technology, the field of rehabilitation medicine has also generated massive and diversified big data. However, the current level of utilization of rehabilitation medicine is relatively low. Empowering the development of rehabilitation medicine through big data is crucial. In order to better promote the standardized development of rehabilitation big data in China, the Rehabilitation Big Data Working Committee of the Chinese Rehabilitation Medical Association organized an expert plan on the concept, significance, and necessity of rehabilitation big data; Rehabilitation big data source; Priority areas for rehabilitation big data application research; Basic research methods for rehabilitation big data; The standards and principles for establishing a rehabilitation database; Consensus has been developed on six aspects of clinical application scenarios for rehabilitation big data, providing reference for healthcare workers and researchers related to rehabilitation big data.

Research on Marketing Strategy based on Social Media Big Data

With the rapid advancement of internet technologies, social media platforms have emerged as pivotal channels for information exchange and dissemination. The vast amounts of user-generated data provide businesses with unprecedented insights. The analysis of big data from social media allows for more precise and effective marketing strategies. This paper aims to explore the characteristics of big data from social media and analyze its applications in marketing strategies. By delving into data types, volumes, structures, and user behavior, it reveals the main challenges businesses face in big data applications and proposes corresponding solutions. Precision marketing, brand management, market trend forecasting, and crisis management are significant areas of application for big data in marketing. This paper, through a detailed examination of these strategies, highlights the potential and challenges of big data in contemporary marketing. Ultimately, it seeks to offer theoretical support and practical guidance for businesses on how to develop and implement effective marketing strategies in a big data-driven market environment.

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.

Optimization and performance improvement of distributed data storage in hybrid storage systems

With the rapid development of information technology, the storage and processing of massive data has become one of the important challenges facing the current computing field. Traditional centralized storage systems have been unable to meet the needs of big data applications, while distributed storage systems have become the infrastructure of modern data centers because of their high scalability and high availability. However, in practical applications, a single distributed storage model is often difficult to balance cost-effectiveness and performance requirements. Therefore, this paper proposes a hybrid storage system model combining local storage and cloud storage resources and discusses a series of optimization strategies for this model. The focus of the research is to improve the overall performance of the system through the design of intelligent data sharding, redundancy and fault tolerance mechanisms, and the application of effective load balancing technology. The effectiveness and superiority of the proposed method are verified by testing and analyzing several typical application scenarios on the experimental platform. The experimental results show that the optimized hybrid storage system not only significantly improves the speed of data access, but also effectively reduces the storage cost, demonstrating its potential in future large-scale data management.

A Review on Big Data Applications and their Challenges

A Review on Big Data Applications and their Challenges

Research on modeling method for digitizing distribution substation areas based on big data from smart meters

With the wide application of smart meters, real-time data collection in power grid operation can be carried out in real time, which provides big data support for the digital modeling of distribution station areas. At the same time, the digital transformation of the distribution area can be promoted through the digital modeling of the distribution area. In this situation, how to use the big data of smart meters to achieve the digital modeling of the distribution area needs to be further studied. Firstly, this paper briefly introduces the connotation of digitalization in the distribution station area. At the same time, aiming at the digitalization of the distribution station area, it analyzes the digitalization of station features, user features, new energy features, network parameters and operation features, and obtains the digitalization model. Finally, on the basis of the above, select an application scenario to introduce the application of digitalization modeling. The research results can provide a reference for the combination of big data application of smart meters and digitalization of the distribution area, and help the digital transformation of the distribution area.

Digital Inclusive Finance Supports the Modernization of Agriculture Development

In the process of financial development, the high threshold and credit asymmetry not only increase the difficulty and cost of financing in rural agriculture, but also hinder the upgrading of agricultural industrial structure and delay the development of agricultural modernization. With the development of information technology and the wide application of big data, digital inclusive finance has become an important path and inevitable choice to promote the upgrading and modernization of China's agricultural industrial structure. The project intends to use the entropy method to measure the development level of agricultural modernization; Based on the two-way fixed effect model, the paper explores the effect of digital inclusive finance on agricultural modernization. With the help of the intermediary effect model, this paper explores the internal mechanism of the influence of the digital inclusive financial policy on the development of rural modernization from the level of "the upgrading of agricultural industrial structure". Based on the empirical results, this paper analyzes the heterogeneity of the effects of digital financial inclusion policies from the two dimensions of rural location and rural characteristics, and explores an effective path to promote digital financial inclusion and industrial upgrading in rural areas with prominent internal differences, with a view to providing reference and basis for the formulation and implementation of future digital financial inclusion and agricultural modernization development policies.

Review of voice biomarkers in the screening of neurodegenerative diseases

Abstract Neurodegenerative diseases significantly impact patients and their families, making early identification crucial for improving patients’ quality of life and reducing care burdens. Current screening methods for neurodegenerative diseases, such as dementia and mild cognitive impairment, still rely on subjective assessments or expensive techniques like invasive cerebrospinal fluid analysis and magnetic resonance imaging. These factors make early identification challenging. Voice biomarkers present a promising alternative as convenient, non-invasive, and low-cost screening tools. With the application and development of artificial intelligence and big data, the prediction and screening of neurodegenerative diseases based on voice data have become a research focus. This article reviews the progress in voice biomarkers for neurodegenerative diseases screening and classification. It summarizes relevant studies on both single and multimodal data, identifies existing challenges, and suggests future research directions to enhance the application of voice biomarkers in neurodegenerative disease contexts.

Applications for Big Data.

Applications for Big Data.