Big Data Applications Research Articles

The Transformative Impact of Big Data in Healthcare: Improving Outcomes, Safety, and Efficiencies

The integration of Big Data analytics into the healthcare sector has initiated a period of unparalleled transformation, significantly influencing patient care, research, and healthcare administration. This detailed synthesis, derived from a systematic literature review of 31 articles published between 2013 and 2023, examines the extensive impact of Big Data within the healthcare industry. It addresses how Big Data enhances personalized medicine and optimizes treatment protocols, promotes the use of predictive analytics for the early detection and management of diseases, and bolsters patient safety through the implementation of real-time monitoring and decision support systems. The review also confronts the challenges and limitations associated with Big Data's application, including concerns over data privacy and security, ethical dilemmas, interoperability issues, and the necessity for both skilled personnel and advanced technological infrastructure. Furthermore, this overview ventures into future prospects and technological advancements, emphasizing the pivotal role of emerging technologies such as artificial intelligence (AI), machine learning, and blockchain in heralding a new era of healthcare. It highlights Big Data's instrumental role in supporting public health measures and preparing for pandemics, alongside forecasting the ongoing influence of Big Data in fostering innovative healthcare paradigms. This analysis advocates for a concerted effort among healthcare professionals, policymakers, and technology experts to fully leverage Big Data's capabilities in healthcare, aiming to enhance health outcomes, increase operational efficiencies, and ensure the long-term viability of healthcare systems.

Scalable fuzzy multivariate outliers identification towards big data applications

Data outliers is intrinsically a fuzzy concept and should be treated as such. This paper is a continuation of a research on fuzzy outliers. Extending the BACON algorithm, FBACON1 and FBACON2 have been proposed as fuzzy solutions to the crisp decision boundary of BACON. This paper investigates the scalability potentials and drawbacks of FBACON1 and FBACON2 in Big Data. The investigation concluded that the sensitivity of FBACON2 towards Big Data. Therefore, this paper introduces FBACON3 as a more scalable solution than FBACON2. Three performance measures have been introduced to compare the performance of the three solutions. The study shows that FBACON1 provided the best computational performance followed by FBACON3. However, in terms of the other two measures FBACON2 and FBACON3 are tied but they outperformed FBACON1. Considering the sensitivity towards Big Data volumes and the computation time, FBACON3 is a better candidate than FBACON2. Code metadataPermanent link to reproducible Capsule: <https://doi.org/10.24433/CO.6812430.v1; >.

Research on Learning Social Networks and Collaboration in Education Using Big Data Analytics and Machine Learning

This paper explores the application of big data analytics and machine learning in education, particularly in the context of learning social networks and collaborative research. By systematically reviewing existing literature and conducting theoretical analysis, this study delves into how big data and machine learning technologies can support and optimize learning behaviors and collaboration patterns within educational social networks. Using literature review and theoretical deduction methods, the research analyzes the potential applications of big data analytics in educational data processing, learning behavior prediction, and personalized learning recommendations. Additionally, it examines the contributions of machine learning algorithms in identifying and optimizing node relationships, information dissemination paths, and collaboration efficiency within learning social networks. By comparing domestic and international research outcomes and considering current social trends and policy directions, several theoretical models and analytical frameworks are proposed to provide theoretical support and reference for future educational research. The results indicate that big data analytics and machine learning technologies not only enhance the efficiency of educational data processing, but also significantly improve information flow and collaboration quality within learning social networks, thereby promoting the development of personalized and collaborative learning. This theoretical analysis offers new perspectives and approaches for further exploring the applications of big data and machine learning in the education sector.

A bibliometric study with statistical patterns of industry 4.0 on business management in the decade

In search of perpetual sustainability, companies need a continuous line of innovation, incorporating new technologies to optimize production; the adoption of Industry 4.0 is marking an important milestone in the deployment of business functions in all areas, adapting from human resources management to manufacturing operations. By leveraging these tools, operational areas can be interconnected to drive productivity. Thus, this research performs a descriptive bibliometric analysis of the influence of Industry 4.0 on business management from 2013 to 2022, using the contribution of physics through the implementation of data mining tools, such as Bibliometrix, whose foundation is given by mathematical and statistical models; in such a way, articles indexed in Scopus and Web of Science were analyzed, focusing on the indicators of citations, bibliography, abstract and keywords. The results showed an exponential growth of articles on Industry 4.0, with manufacturing as the central area of interest, especially in artificial intelligence and big data applications. The most cited articles highlight its role in process optimization. Overall, this bibliometric analysis suggests that the adoption of Industry 4.0 has a positive impact on decision-making, improving the direction of business management.

Design and Practice of The Fire IoT Big Data Application Platform

Design and Practice of The Fire IoT Big Data Application Platform

A study on the action mechanism of digital transformation on the output quality of technological innovation

The fourth Industrial Revolution is in full swing, the whole world is pondering the same question: Where is the core breakthrough? With the continuous breakthrough and extensive application of cloud computing, big data, artificial intelligence and other next-generation information technologies, One sign has emerged: digital economy is becoming a new driving force for global economic growth. The impact of digital transformation on enterprises is to improve the quality and efficiency、Improve information quality, Or spread out limited resources and reduce productivity, There is no consensus among academics. To fill that gap, this paper aims to sort out the internal mechanism of how digital transformation improves the output quality of the technological innovation of enterprises from a theoretical perspective, and to establish a digital transformation index at the micro level. On this basis, this study empirically examines how digital transformation affects the output quality of technological innovation.we adopts panel data. It uses data collected from 159 manufacturing enterprises in China. By adopting the methods of PLS structural equation and fuzzy set qualitative comparative analysis (fsQCA), it empirically tests the research model and conducts a mediation analysis through regression analysis. The findings indicated that, Digital transformation can help manufacturing enterprises to improve the output quality of their technological innovation; digital infrastructure plays a significant mediating role between digital transformation and the output quality of technological innovation; the investment of manufacturing enterprises in software and hardware digital infrastructure have a positive impact on the output quality of technological innovation. There is a complementary relationship between the two. The paper was conducted in the context of Chinese culture and can be generalized to other Asian countries. We believe that the research conclusions of this study have important practical significance for how manufacturing enterprises can effectively carry out digital transformation and improve the output quality of technological innovation.

Big Data Applications and Mining in the Healthcare Field

The healthcare sector faces unprecedented challenges due to global population growth, aging trends, and the continuous outbreak of diseases. This paper explores the significance and potential of big data applications in healthcare. We discuss challenges such as population aging, chronic disease management, and infectious disease transmission, highlighting big data's role in addressing these issues. We examine big data application mining methods, including data collection, storage, preprocessing, cleaning, and analysis, with applications in disease prediction, early diagnosis, clinical decision support, and epidemiological research, illustrated through case studies. Challenges encompass data privacy, ethics, data cleaning, integration, and model interpretability, necessitating continuous technological innovation. Future trends include enhanced data privacy, technological innovation, and interdisciplinary collaboration. Collaboration among research institutions, healthcare organizations, and government agencies is encouraged to advance big data application mining and contribute to healthcare progress. Overcoming challenges and embracing opportunities promises a healthier and more prosperous future.

Big data applications in portfolio management: A review of techniques and strategies

In the ever-evolving landscape of finance, the advent of big data has ushered in a paradigm shift, transforming the art and science of portfolio management from its traditional moorings to a future where data reigns supreme. This paper delves into the intricate dance between big data and portfolio management, aiming to demystify the journey from historical practices to the forefront of technological innovation in finance. Through a meticulous examination, the study sets out to map the evolution of portfolio management in the digital age, scrutinize the technological foundations essential for big data analytics, and explore the symbiotic relationship between big data capabilities and investment strategies. Employing a systematic literature review as its methodological backbone, the research navigates through the complexities of integrating big data into portfolio management, offering a panoramic view of the current landscape and the challenges that lie ahead. The findings illuminate the transformative impact of big data analytics, highlighting its potential to enhance decision-making, optimize risk assessment, and unearth new investment opportunities. However, the journey is fraught with challenges, including data privacy concerns, ethical considerations, and the need for continuous technological adaptation. In conclusion, the study advocates for a proactive embrace of big data, urging portfolio managers to cultivate a culture of innovation, ethical vigilance, and lifelong learning. As the financial sector stands on the brink of a data-driven era, this paper calls for a strategic reorientation towards the integration of big data analytics in portfolio management, paving the way for a future where informed decision-making and strategic foresight are paramount.

Reviewing the use of big data in supply chain optimization in the USA

This paper provides a succinct overview of the review conducted on the utilization of Big Data in supply chain optimization within the United States. The advent of Big Data analytics has revolutionized traditional supply chain management, offering unprecedented opportunities for enhancing efficiency, reducing costs, and improving overall operational performance. This comprehensive review examines the current state of Big Data applications in supply chain optimization, focusing specifically on the context of the United States. The review begins by outlining the fundamental role of Big Data in transforming supply chain processes. It delves into various aspects, including data collection, processing, and analysis, emphasizing their impact on decision-making and strategic planning. Notably, the study highlights real-world examples of successful Big Data implementations within the U.S. supply chain landscape, showcasing how companies have leveraged data-driven insights to streamline operations and gain a competitive edge. Furthermore, the paper explores the challenges associated with the adoption of Big Data in supply chain management. It addresses concerns related to data security, privacy, and the need for advanced technological infrastructure. Additionally, the review considers the organizational and cultural shifts required for successful integration, emphasizing the importance of a holistic approach to implementation. The paper concludes by pointing towards future trends and potential developments in the use of Big Data for supply chain optimization in the U.S. It underscores the evolving nature of technology and its continued impact on reshaping supply chain strategies. The insights derived from this review contribute to a deeper understanding of the current landscape, offering valuable implications for practitioners, researchers, and policymakers engaged in optimizing supply chain processes through the harnessing of Big Data analytics.

Key factors driving the potential future developments in automated manufacturing at Hindustan aeronautics ltd (HAL)

This research aims to explore the key factors driving the application of Big Data and IoT/AI in potential future developments of automated manufacturing at Hindustan Aeronautics Ltd (HAL). By investigating the current state of automated manufacturing, the study will identify the benefits and challenges associated with integrating Big Data, IoT, and AI technologies. The research questions will focus on understanding the factors influencing the adoption of these technologies, their impact on manufacturing processes, and the potential benefits they offer to HAL. The research objectives include analysing the current landscape of automated manufacturing, identifying key factors driving the application of Big Data and IoT/AI, assessing the benefits and challenges of implementing these technologies, and providing recommendations for HAL's future developments in automated manufacturing.

Exploration and Practice of Big Data Introductory Courses for Big Data Management and Application Majors

As an introductory course for the emerging major of big data management and application, “Introduction to Big Data” has not yet formed a curriculum standard and implementation plan that is widely accepted and used by everyone. To this end, we discuss some of our explorations and attempts in the construction and teaching process of big data courses for the major of big data management and application from the perspective of course planning, course implementation, and course summary. After interviews with students and feedback from questionnaires, students are highly satisfied with some of the teaching measures and programs currently adopted.

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.

As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

Design and Application of Legal Information Systems Based on Big Data Technology

China's exploration of the legal application of big data is still far from thorough enough. This article proposes a universal suitable for hierarchical big data storage systems, which can quickly add new cache policies when needed and provide cache scheduling strategies. The neural training algorithm used in legal information systems implements a complete parallel computing framework for large-scale neural network training, supporting distributed storage and management of large-scale sample data. The experimental results show that the framework has good scalability and fault tolerance, and can quickly train legal information systems, improving their efficiency and response speed. This provides new ideas and methods for the design and development of legal information systems.

A big data driven vegetation disease and pest region identification method based on self supervised convolutional neural networks and parallel extreme learning machines

A self supervised convolutional neural network-parallel extreme learning machine classification model based on big data is proposed to address the subjectivity and inaccuracy of traditional methods for identifying vegetation pests and diseases that rely on manual observation and empirical judgment. This model is constructed using convolutional neural networks and parallel extreme learning machines, and integrates feature extraction networks with dual attention mechanisms to improve the accuracy of identifying pests and diseases. The model utilized a large amount of big data for training, achieving a recall rate of 98.42 % on multispectral datasets, and an overall classification accuracy of 99.04 %. After optimizing the residual network, the overall accuracy of identifying vegetation pest and disease areas has been further improved to 99.77 %, and the recall rate has also reached 98.91 %. These results indicate that the method proposed in this study has high accuracy and efficiency in the application of big data, can meet the needs of disease and pest identification, and provides effective technical support for the monitoring and prevention of crop diseases and pests, which has important practical significance.

From Data to Action: Exploring Technological Interventions in Climate Change Mitigation

This research paper critically examines innovative approaches to climate change mitigation by integrating big data and technological interventions. Emphasizing the pivotal role of system-of-systems thinking, the study underscores its significance in addressing the complexities of mitigation challenges. By integrating diverse disciplines, data, and tools, system-of-systems thinking promotes a comprehensive understanding and necessitates deeper collaboration between industry initiatives and scientific research. Big data emerges as a powerful tool, contributing to climate change research and decision-making by modeling scenarios, transforming energy practices, and enhancing food and water security. The utilization of initiatives like the Intergovernmental Panel on Climate Change and the Global Ocean Observing System fills crucial gaps in scientific, technical, and socio-economic data, enabling researchers to gain valuable insights into the effectiveness of various mitigation strategies. This study explores the application of big data and technological interventions across three primary domains of climate change mitigation: agriculture, cleaner production, and climate resilience. It categorizes emerging climate change mitigation technologies. In conclusion, the integration of system-of-systems thinking, big data, and emerging technologies offers a holistic and effective approach to addressing climate change mitigation challenges, empowering researchers and policymakers to devise comprehensive strategies for a sustainable future.

Cross-Cultural Applications of Big Data and Artificial Intelligence in Fashion Design

Cross-Cultural Applications of Big Data and Artificial Intelligence in Fashion Design

Performance Evaluation of Intelligent Agricultural Supply Chain Based on Structural Equation Model

To solve the problems of unstable chain and poor supply chain (SC) function in the China’s agriculture industry, the relevant study is carried out on the function evaluation of the sustainable agricultural SC by structural equation modelling (SEM) under the background of digital technology. Primarily, the analysis is implemented on the research status of the sustainable SC and SC function. Then, starting with the relationship among big data (BD) application, information sharing, and sustainable SC function of agricultural production, a function evaluation model is implemented for the sustainable SC of SEM-based agricultural production. Next, a questionnaire is designed for the research on the impact of BD applications on the function of the sustainable agricultural SC. Ultimately, the statistical analysis of the obtained data reveals the correlation between the interaction and influence among the BD application, information sharing, and sustainable SC function of agricultural production. The results show that the BD application dimension, information sharing dimension, and all sub-dimensions have is beneficial to the function of the agricultural sustainable SC. The application of multidimensional cloud technology in BD has no obvious relationship with the dimensionality and level of information sharing, and the relationship between other corresponding dimensions shows a significant positive impact. A referenceable method is provided for the practice of sustainable SC management of agricultural production.

Big data: Application, potential problem, and prospect

In recent years, big data has gradually become a hot topic; its appearance makes people full of infinite reverie about future life. Big data, is not just a theory but has gradually begun to affect all aspects of peoples lives. Many previous articles focused on the analysis principle and operation mechanism of big data and did not cover the practical application of big data too much. The specific application of big data is still a mystery to people. This paper uses literature analysis to describe the application, potential problems, and prospects of big data. This paper finds that big data, on the one hand, has appeared in all aspects of people's daily lives, which greatly facilitates people's lives, on the other hand, it also hides some problems. But risks always coexist with opportunities, if people can seize opportunities and control risks, big data will make a huge difference in people's lives.