Big Data Technology Research Articles

The role of big data technology in project schedule management: the case of Amazon

With the rapid development of the Internet, big data technology has received widespread attention in the field of management. Traditional project schedule management methods cannot adapt to the complex and changing environment, and some researchers believe that big data technology has become a key tool to improve and optimize project management. However, there is a lack of mature theoretical systems and practical experience to guide the application of big data technology. Based on the case of Amazon, this paper analyses how the company uses big data technology to optimize project schedule management. It is found that the application of big data technology in project management brings significant benefits. It is able to provide empirical and innovative support for project schedule management. This paper discusses the application of big data technology in project schedule management, highlighting its capability to enable timely adjustments. Real-time monitoring and forecasting facilitate proactive management of project directions and potential risks. Moreover, it leverages real-time data to optimize resource allocation and enhance resource utilization. Furthermore, the paper addresses the challenges and proposes countermeasures associated with big data technology in this context. To tackle the issue of uneven data quality, a robust data collection mechanism should be established.

Innovative Application of Big Data Technology in New Media Personalized Service

With the rapid development of big data technology, its application in the field of new media is becoming more and more extensive, especially in personalized service shows great potential. This paper discusses in depth the application, challenges and future development of big data technology in new media personalized service. The following conclusions are drawn: (1) Big data technology plays a key role in new media personalized service Big data technology can construct a refined user profile, realize personalized content recommendation, and improve user satisfaction and loyalty by collecting, analyzing and utilizing user behavioral data on new media platforms. (2) Big data technology faces multiple challenges in new media personalized services. Although big data technology shows great potential in new media personalized service, it still faces challenges in data privacy and security, data quality and accuracy, technology update and algorithm optimization, as well as regulatory compliance and ethics. (3) Innovative applications promote the development of big data technology in new media personalized services. With the continuous innovation and application of big data technology, new media personalized services are also developing and improving.

Personalization at Scale: Big Data Technologies in Digital Content Delivery and Their Effect on Viewer Experience

This article examines the transformative impact of big data technologies on the digital content delivery industry, focusing on their application in enhancing customer experience through video streaming services. Through a comprehensive case study of a leading provider, we investigate the implementation of Hadoop and AWS Big Data product suites to optimize content delivery, personalize user recommendations, and enable dynamic ad insertion. Our findings demonstrate significant improvements in service reliability, viewer engagement, and advertising effectiveness. The article reveals how Big Data analytics facilitate real-time content optimization and personalized user experiences, increasing viewer retention and satisfaction. Furthermore, we explore the challenges and limitations encountered during implementation, providing insights into the practical applications of Big Data in the evolving landscape of digital media consumption. This article contributes to the growing body of literature on Big Data applications in media and entertainment, offering valuable insights for practitioners and researchers in digital content delivery.

Risk Prediction and Prevention Strategies Used by the Financial Industry Are Supported by Artificial Intelligence Technologies

With the advancement of artificial intelligence (AI) and big data technology, traditional financial management faces challenges in meeting modern needs. China's financial sector, although late to start, is embracing AI to mitigate risks like business and credit risks. This study explores risk prediction and avoidance methods in finance using AI. It employs elementary catastrophe theory to analyze dynamic system discontinuity, identifying high-risk periods when the calculated financial stress index (FSI) exceeds 1. Each unit increase in FSI affects its lag period by 0.7327 units. Macro-economic pressure, reflected in the macroeconomic pressure index (MSI), significantly impacts FSI, with the current period having a more pronounced effect than the lag period. Financial institutions must evolve talent into hybrid professionals and invest in AI to enhance risk management while pursuing profitability.

The Application of Big Data Technology in Supply Chain Cost Control: The Case of IKEA

Abstract: Supply chain management is important in all aspects of business processes. With the advent of the information age, big data technology has become important in developing all aspects of our country. Appropriate use of big data technology in supply chain cost management by enterprises can not only improve their ability to analyse and master data but also timely control the cost expenditure issues in different links of the supply chain, improve management systems, and enhance the companys core competitiveness. In order to improve the efficiency and accuracy of supply chain management, this study explores the application of big data technology in enterprise supply chain cost control and data analysis. This study uses big data as the background to analyse the costs of several important links in the supply chain of IKEA. It combines the discovered problems with big data technology to improve them, hoping to be helpful to the development of IKEA and related industries.

Application and Research of Big Data Technology in Intelligent Ward

Application and Research of Big Data Technology in Intelligent Ward

A Smart Healthcare System for Remote Areas Based on the Edge–Cloud Continuum

The healthcare sector is undergoing a significant transformation due to the rapid expansion of data and advancements in digital technologies. The increasing complexity of healthcare data, including electronic health records (EHRs), medical imaging, and patient monitoring, underscores the necessity of big data technologies. These technologies are essential for enhancing decision-making, personalizing treatments, and optimizing operations. Digitalization further revolutionizes healthcare by improving accessibility and convenience through technologies such as EHRs, telemedicine, and wearable health devices. Cloud computing, with its scalable resources and cost efficiency, plays a crucial role in managing large-scale healthcare data and supporting remote treatment. However, integrating cloud computing in healthcare, especially in remote areas with limited network infrastructure, presents challenges. These include difficulties in accessing cloud services and concerns over data security. This article proposes a smart healthcare system utilizing the edge-cloud continuum to address these issues. The proposed system aims to enhance data accessibility and security while maintaining high prediction accuracy for disease management. The study includes foundational knowledge of relevant technologies, a detailed system architecture, experimental design, and discussions on conclusions and future research directions.

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

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.

Proactive pricing strategies for on-street parking management with physics-informed neural networks

Effective pricing is important for on-street parking management and proactive parking pricing is an innovative strategy to achieve optimal parking utilization. For proactive parking pricing, accurately predicting parking occupancy and deriving the price elasticity of parking demand are necessary. In recent years, there have been an increasing number of studies applying big data technology for parking-occupancy prediction. However, existing research has not incorporated economic knowledge into modeling, thus preventing application of the price elasticity of parking demand. In this study, proactive pricing strategies are proposed to adjust on-street parking prices which involve a parking-occupancy prediction model and a price-optimization method. Physics-informed neural networks are employed to achieve accurate prediction of parking occupancy and calculation of parking price elasticity. An elasticity-occupancy parking-management strategy is proposed for on-street parking management which leverages parking occupancy and price elasticity to guide pricing interventions. A case study shows that the parking-occupancy prediction model can make accurate predictions and derive the price elasticity of parking demand. Proactive parking pricing enables drivers to plan their trips in advance, allowing parking occupancy within an optimal range.

Legitimacy and Limits of Administrative Power in the Big Data Era

The advent of the big data era has profoundly transformed the exercise of administrative power, enhancing the efficiency and accuracy of administrative decisions while simultaneously posing challenges to legitimacy and the potential for abuse of power. This study examines the impact of big data technology on administrative power, systematically analyzing the foundations and limits of administrative power's legitimacy. It explores how to achieve a balance between legitimacy and limits of administrative power in the context of big data. By examining the intersection of traditional theories of power legitimacy and big data technology, the study proposes strategies for enhancing transparency in administrative decisions, strengthening public participation, and promoting inter-departmental cooperation. It also looks forward to innovative paths for exercising administrative power in the future. The results of this study are significant for both administrative management practices and theoretical development in the big data era.

Course Reform and Practice of "Python Programming" for Artificial Intelligence and Big Data Applications

With the rapid development of artificial intelligence (AI) and big data technologies, Python has become a widely used programming language in related fields. However, the traditional "Python Programming" course has been unable to meet the demands of applications in AI and big data in terms of content and teaching models. Therefore, this paper presents a comprehensive reform of the "Python Programming" course, optimizing course content by incorporating modules on big data processing and artificial intelligence. Additionally, a project-driven teaching model and a diversified evaluation system have been implemented. By introducing innovative teaching tools and resources, students' practical abilities and innovative thinking have been enhanced. The practical outcomes of the course reform are significant, with students showing notable improvements in their programming skills in AI and big data. In the future, this study will continue to optimize course content to address the ever-changing technological demands.

Monitoring system for physical activity and fitness based on service robots and biomechanics

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

Tribute VS "Nanman": The Image Construction of "Magic Mirror Type"

For over 2000 years, feudal dynasties throughout China have referred to southerners as the “Nanman” (means Southern Barbarian). However, the specific situation of the treatment of Nanman titles by various dynasties has not been explored. In addition, the Nanman never stopped paying tribute to the Central Dynasty. No one is involved in whether tribute can earn the respect that the dynasty deserves. Nowadays, big data technology can easily handle massive amounts of literature, and it is entirely possible to analyze the above issues from "official histories" (the 25 historical books) using the method of word frequency research. The words frequency shows that since the unification of Lingnan by Emperor Qin Shi Huang, the feudal dynasties presented a paradox to Lingnan: The products in Lingnan were coveted, and local customs were despised. "Tribute" was used to cover up the predatory nature of Kings, and "Nanman" was used to establish political, national and cultural rank. The number of "tribute" and the meaning of "Nanman" formed the "magic mirror" development relationship in the big tree of feudal autocracy until the end of the Qing Dynasty. But on the one hand, the positive factors of "tribute" were absorbed and used by the western powers to establish their own alliances. On the other hand, they also mislead the public opinion and create trouble. Although "Nanman" has been eradicated politically, the regional discrimination and cultural misreading it contains have not been eradicated and often occurs. "Tribute" ranges from Kyushu to overseas to international relations. The connotation of "Nanman" from the orientation, nation and culture, the meaning of these two words has been spontaneously changing. It is timely to clarify their relationship and help make China revive.

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.

The foresight methodology for transitional shale gas reservoirs prediction based on a knowledge graph

The sedimentary environment in the eastern Ordos area of China is complex and contains a large number of transitional facies environments. Yet, there are many characteristics such as large vertical lithology changes, complex lateral sedimentary environment changes, small monolith thickness and large organic matter content changes, which lead to large uncertainty in the prediction of favorable areas for transitional shale gas. As the intricate reservoirs continue to unfold, the conventional linear prediction methods find themselves facing an arduous path to meet the demands of development. The ever-evolving complexity of these reservoirs has outpaced the capabilities of these traditional approaches. It becomes apparent that a more comprehensive and adaptable approach is necessary to navigate the intricacies of these reservoirs and unlock their hidden potential. Therefore, we put forward a method of introducing knowledge graph into transitional shale gas reservoir prediction in the eastern Ordos region by using big data technology. Because artificial intelligence big data relies heavily on data tags, it is particularly important for the construction of tags. Firstly, a top-down knowledge graph in the field of reservoir prediction is constructed to determine the key parameters used in prediction, namely porosity, total organic carbon and brittleness index. Secondly, the decision tree knowledge graph optimization label is constructed in a bottom-up way. The key parameter of this prediction is the knowledge graph obtained according to the professional knowledge of reservoir prediction, so as to optimize the school label of U-net and reduce the workload of artificial judgment. The results of the combination of the two methods are applied to 11 wells in Daji area of Ordos, and the experimental results are consistent with the actual situation of the reservoir. Based on the foundation of theoretical knowledge, this method enhances the efficiency and accuracy of interpretation and evaluation. It provides fundamental and technical support for the selection of favorable areas in shale gas exploration and the evaluation of exploration and development prospects, particularly in transitional shale gas areas, which is innovative and advanced in the field.

Research on Education and Teaching of Artificial Intelligence Enabling Intelligent Construction

This study is committed to in-depth analysis of the practical application of artificial intelligence in intelligent construction teaching and its empowerment effect. An efficient and intelligent teaching platform is built, which can intelligently recommend relevant learning resources according to students’ learning progress and feedback, and track students’ learning situation in real time. This personalized learning style not only improves the learning efficiency of students, but also enables them to better adapt and master the knowledge and skills in the field of intelligent construction. The introduction of artificial intelligence technology has innovated the method of intelligent construction teaching. Based on the learning analysis technology of big data, we can conduct in-depth analysis of students’ learning behaviors and achievements, so as to grasp students’ learning status and needs more accurately. These achievements not only show the great potential of artificial intelligence technology in the field of education, but also provide new ideas and methods for the reform and innovation of intelligent construction teaching.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Healthcare Data Privacy Protection Through the Lens of Big Data

In the era of rapid development of big data, there are more and more fields that have a close connection with big data technology, of which the medical field is a particularly important part. Nowadays, citizens reflect that the medical information they had uploaded to the cloud platform has been leaked frequently, even in some regular hospitals and other government-provided platforms, which has aroused citizens' doubts and dissatisfaction. This paper will combine the two perspectives of the field of medical information and big data technology to provide new countermeasures and solutions for solving the problem of medical data leakage from the perspective of big data technology and combining with citizens' awareness of privacy protection.

Digital-Twin Predictive Control of Nonlinear Systems With Time Delays, Unknown Dynamics, and Communication Delays.

With the advancement of computing technology and big data technology, digital twins have gradually been applied in various fields, such as manufacturing, energy, and healthcare. This article studies the predictive control of nonlinear dynamic systems using digital twins. Based on a digital-twin control system framework, predictive control is discussed for three different nonlinear systems with time delays: 1) known nonlinear systems; 2) unknown nonlinear systems; and 3) unknown nonlinear cyber-physical systems. Both a digital-twin predictive control strategy and a digital-twin control predictor are proposed to compensate for time delays and communication delays actively. With the strategy and predictor, the digital-twin controller of a time-delay nonlinear system can be designed to achieve the desired performance based on the nonlinear system without time delays, which vastly simplifies the controller design procedure. A digital model is constructed using data to deal with unknown nonlinear dynamics. The three different closed-loop digital-twin predictive control systems are analyzed to derive a unified stability criterion. The simulation results show how the proposed digital-twin predictive control method performs well for nonlinear systems with time delays, unknown dynamics, and/or communication delays.