Big Data Platform Research Articles

A Survey on the Utilization of Big Data in the Healthcare Sector and the Demand for Integrated Data

Objectives: To investigate the status of secondary data and big data platform utilization and the demand for integrated data in the health care sector.Methods: An online survey was conducted using a structured questionnaire targeting members of six academic societies in the healthcare field. A total of 220 researchers responded to the survey.Results: 88.2% of respondents said they had experience using secondary data sources in the healthcare field, and 19.0% responded that they had experience linking data using the healthcare big data platform. Among the data in the health and medical field, the most used dataset was health insurance claims data, and if linking between dataset, the most desired linked data type is ‘National Health and Nutrition Examination Survey-Health Insurance Claims Data–National Health Examination-Death Information’. The advantages of linked data on big data platforms were ‘large volume of big data or representative’, ‘diversity of data’, and ‘long-term follow-up’. The major items requiring improvement were ‘delay in time to provide data’, ‘limitation of data provision range’, and ‘low linkage rate between dataset’.Conclusions: To promote the use of linked data in the health care field, comprehensive efforts are needed, including rapid data provision, improvement of the combination rate, standardization of data, and establishment of ethical guidelines.

Practices and barriers for big data projects

The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.

Study on the coordinated development of national traditional sports and tourism brands based on big data platforms from the perspective of “The Belt and Road”

The Belt and Road (B&R) plan is put out within the framework of global economics and strategic growth. This study examines the written material of popular tourist sites along B&R and the tourism assets from the viewpoint of B&R, based on the wireless network and AI technology, and using a big data platform and the Internet of Things (IoT) User Generated Content (UGC) network structure. To manage tourist pictures from customers’ views, online travel notes are first utilized as examples. Next, tourism texts’ keywords are extracted using Python big data and AI technology to understand consumers’ perceptions of scenic spot preferences, tourism facilities and services, and social and cultural customs. The findings demonstrate that, when compared to the conventional tourism brand development strategy, the integrated development strategy based on the AI big data platform can not only increase the effectiveness of managing tourists’ perceptions of scenic locations but can also encourage the common development of national sports event components and intelligent tourism image management. Several sports tourist boutique picturesque locations have also been built along B&R following years of development of intelligent tourism and sports projects, which will strengthen the effect of multicultural exchanges.

A learning-based framework for spatial join processing: estimation, optimization and tuning

The importance and complexity of spatial join operation resulted in the availability of many join algorithms, some of which are tailored for big-data platforms like Hadoop and Spark. The choice among them is not trivial and depends on different factors. This paper proposes the first machine-learning-based framework for spatial join query optimization which can accommodate both the characteristics of spatial datasets and the complexity of the different algorithms. The main challenge is how to develop portable cost models that once trained can be applied to any pair of input datasets, because they are able to extract the important input characteristics, such as data distribution and spatial partitioning, the logic of spatial join algorithms, and the relationship between the two input datasets. The proposed system defines a set of features that can be computed efficiently for the data to catch the intricate aspects of spatial join. Then, it uses these features to train five machine learning models that are used to identify the best spatial join algorithm. The first two are regression models that estimate two important measures of the spatial join performance and they act as the cost model. The third model chooses the best partitioning strategy to use with spatial join. The fourth and fifth models further tune two important parameters, number of partitions and plane-sweep direction, to get the best performance. Experiments on large-scale synthetic and real data show the efficiency of the proposed models over baseline methods.

Hadoop-Based Political and Ideological Big Data Platform Architecture and Pattern Mining in Higher Education

There is a growing opportunity for self-improvement within the realm of higher education, represented by the combination of big data technologies and higher education. The Education Big Data Processing Framework (EBDPF) is the name of the platform that is the subject of this article, which focuses on the development of an advanced big data processing platform. In order to process huge datasets in an effective manner, the platform makes use of Hadoop's big data storage architecture, in addition to Hive, Flume, and Sqoop for data collection and synchronization, respectively. In this study, the implementation of standard data mining techniques is investigated through the use of MapReduce programming. More specifically, the study investigates the execution efficiency and scalability of these algorithms within the Hadoop platform. The data clustering task in data mining is chosen as a representative problem in this study so that the effectiveness of the EBDPF may be evaluated accordingly. An implementation of the clustering job that is based on MapReduce is created and tested on the Hadoop platform. Extensive experiments are conducted with a range of cluster sizes and data sizes. The findings provide more evidence that Hadoop distributed systems are more efficient and successful than traditional methods when it comes to managing data mining applications. The extended performance analysis of computing power not only provides evidence that the platform is currently effective, but it also provides a signal that it has a significant amount of potential that has not yet been utilized. At the convergence of big data technology and higher education, the EBDPF emerges as a strong and promising framework, demonstrating considerable steps in accelerating data processing within educational contexts. In conclusion, the EBDPF is a framework that demonstrates significant progress.

Data-Driven Construction of Operation Evaluation System for Large Medical Equipment in Children's Hospital

Through data collection and analysis, the method of evaluating the operation quality of large medical equipment in children's hospital is explored and suggestions on the use and configuration of large medical equipment is put forward. Collect the equipment operation data through the Internet of Things, and combine the hospital HIS, RIS, HRP and other information system data to establish the operation evaluation system of large medical equipment of children's hospital. CRITIC method is used to quantitatively evaluate single type of equipment and single equipment. Hospital big data platform realizes the longitudinal analysis of the operation data of a single large equipment and forms a visual chart, which is displayed on the PC and mobile terminals. Municipal platform can conduct horizontal analysis on the equipment operation data to realize the comprehensive quantitative evaluation of the operation level of large equipment of children's hospital and put forward suggestions for use and configuration. A large equipment operation evaluation system for children's hospital is established through data collection and analysis, and the fine management level of large medical equipment is improved.

Retracted: Data Analysis Method of Intelligent Analysis Platform for Big Data of Film and Television

Retracted: Data Analysis Method of Intelligent Analysis Platform for Big Data of Film and Television

Platform Supporting Intelligent Human–Machine Interface (HMI) Applications for Smart Machine Tools

As the Internet of Things, artificial intelligence, and the fourth industrial revolution advance, smart factories and machines increasingly gain intelligent features that enable the integration of more sophisticated functionalities. Approaches to achieving this intelligence involve both internal systems, such as human–machine interface (HMI), and external systems, such as big data platforms and cloud services. Although current research leans toward studying external systems, accomplishing intelligent functions through such means poses more challenges in achieving real-time responses during machining processes than using internal systems. When intellectualizing machine tools through internal HMI systems, three critical issues must be addressed. First, HMI functions are structured to depend on the HMI itself, leading to a ripple effect where a problem occurring in one HMI function impacts the entire system. Second, owing to differences in development tools and programming languages, the interconnectivity between functions developed by multiple stakeholders to be loaded onto the HMI may suffer, leading to potential inefficiencies and increased maintenance costs. Third, although various types of computer numerical control (CNC) machines need to communicate with the HMI function, the diverse communication methods and development tools used by each CNC manufacturer result in identical intelligent functions being developed separately for each CNC type. To address these challenges, this study proposes an innovative HMI platform capable of executing and developing various intelligent functions. The HMI platform and its major components are designed and implemented through component-based development (CBD). Subsequently, the performance and effectiveness of the platform are validated using quality attribute scenarios.

Optimizing Data Processing: A Comparative Study of Big Data Platforms in Edge, Fog, and Cloud Layers

Intelligent applications in several areas increasingly rely on big data solutions to improve their efficiency, but the processing and management of big data incur high costs. Although cloud-computing-based big data management and processing offer a promising solution to provide scalable and abundant resources, the current cloud-based big data management platforms do not properly address the high latency, privacy, and bandwidth consumption challenges that arise when sending large volumes of user data to the cloud. Computing in the edge and fog layers is quickly emerging as an extension of cloud computing used to reduce latency and bandwidth consumption, resulting in some of the processing tasks being performed in edge/fog-layer devices. Although these devices are resource-constrained, recent increases in resource capacity provide the potential for collaborative big data processing. We investigated the deployment of data processing platforms based on three different computing paradigms, namely batch processing, stream processing, and function processing, by aggregating the processing power from a diverse set of nodes in the local area. Herein, we demonstrate the efficacy and viability of edge-/fog-layer big data processing across a variety of real-world applications and in comparison to the cloud-native approach in terms of performance.

Development path of college labor education based on big data platform in the context of health psychology

Abstract Labor education is an important content of quality education, and the promotion of higher education cannot be separated from the implementation and practice of labor education. The labor education platform and labor education resource base in colleges and universities are established using the big data platform, which combines Hadoop technology and the HBase database. In order to help students obtain labor education teaching resources more efficiently on the labor education platform, reinforcement learning is combined with a deep neural network to optimize the scheduling of teaching resources, and α -the dispersion recommendation algorithm is used to realize personalized recommendations of teaching resources. With regard to the effectiveness of the labor education platform, load testing and application effects were carried out, and the influencing factors of labor behavior and craftsmanship cultivation of college labor education students were analyzed in depth. The labor education platform’s transaction response time increases by 22.5 seconds when the number of users reaches 5000, according to the results. The personal motivation for craftsmanship in the process of labor education will be affected by the campus culture, and its correlation coefficient is 0.565, and the student’s satisfaction with the platform of labor education reaches 0.825. The innovation and development of labor education in colleges and universities need to fully rely on the big data platform to promote the optimization and sharing of labor education resources and to promote the development of labor education resources. The cultivation of student labor creation and craftsmanship can be ensured through the optimization and sharing of educational resources.

Analysis of the Utility of Chinese Traditional Culture in Contemporary Digital Media Communication Based on a Big Data Platform

Abstract Chinese traditional culture is profound and has important historical and cultural values, and in the digital information era, the dissemination of Chinese traditional culture is facing new challenges and opportunities. This paper begins with the utility mechanism of Chinese traditional culture dissemination and develops a path for Chinese traditional culture dissemination using digital media. On this basis, a digital platform for Chinese traditional culture is established through the modular division of digital platforms and joint visual and interaction design. Then, the hierarchical analysis method (AHP) is combined with (PCA) to complete the weighting. Then, the subjective and objective weights are balanced by the information entropy method (IE). Finally, the comprehensive evaluation model of the utility of digital communication in Chinese traditional culture is constructed by combining with the method of the distance between the superiority and inferiority solutions (TOPSIS), and the evaluation index system of the utility of cultural communication is established. Among them, the benefit value has the highest weight of 0.2007. The other indicators in order of weight are cultural, emotional experience (0.1735), cultural content experience (0.1687), interactive experience (0.1533), social impact (0.1531), and digital function experience (0.1507). Finally, the communication effect of traditional Chinese culture is analyzed using the digital platform of this paper. The average score is 8.75, with excellent communication effects. Based on the utility analysis of digital media communication, this paper provides a digital path for the communication of Chinese traditional culture and establishes a comprehensive evaluation method of communication utility, which provides an effective research method for the utility of digital media communication of traditional culture.

Research on the linkage of “one, two, three, four classrooms” of ideological and political education in colleges and universities based on big data platform.

Abstract To comprehensively improve the quality of ideological and political education in colleges and universities, and to ensure the realization of the fundamental goal of cultivating morality and educating people, this paper emphasizes the importance of the “one, two, three, four classrooms” linkage teaching mode, and proposes an intelligent ideological and political education platform based on big data technology. The platform integrates the first, second, third and fourth classrooms to form an efficient linkage education model. To assess the specific impact of this model on education quality, this study constructed a model using multiple linear regression and estimated the parameters using the least squares method. Through the questionnaire survey and data analysis of five colleges and universities in the university city of L city, it was found that the development quality of the “one, two, three, four classrooms” linkage was generally distributed with the mean value lying in the interval of [0.35,0.55]. Under the baseline regression analysis, the four classrooms had a high R2 fit value of 0.8517 to the dependent variable. The quantile regression analysis showed that as the quantile nodes increased from 0.5 to 1, the number of classroom nodes risen from 1 to 2. Showed that as the quantile node increased from 0.1 to 0.9, the R2 fit value improved more than twofold. This result highlights the need to consider the teaching effect of each classroom comprehensively to ensure that the goal of high-quality development can be realized when colleges and universities promote the “one, two, three, four classrooms” linkage teaching mode of Civics.

Knowledge Graph-based Diversity Analysis of Supplier Holographic Portraits

Abstract Fully understand the development of suppliers in order to make better supplier selection. This paper is based on the knowledge graph, through the knowledge updating of the knowledge graph combined with the Transformer model for knowledge extraction of supplier entity relationship, forming the ternary semantic information of supplier entity relationship. Then, based on the big data platform for the construction of supplier holographic portrait and knowledge storage, through information integration, analysis and other links to identify the supplier attributes for label definition. Taking cell phone product suppliers as an example, we use Python technology to obtain relevant data and validate the specific role of supplier holographic portrait in terms of the supplier’s comprehensive strength, behavioral prediction, transaction closeness, and comprehensive evaluation. The results show that: the correlation between the comprehensive strength of suppliers and the amount of winning bids is strong, and its R2 test result is 0.5924, and it can realize the behavioral prediction of suppliers in the supply chain. Supplier H offers a range of cell phone products in 2022, which is 17.62%<unk>21.17% higher than the benchmark market price. The holographic portrait of suppliers based on a knowledge graph combined with a big data platform can meet the need to carry out an all-around analysis of suppliers and provide more accurate support for diversified decision-making on the demand side.

Study on the Effectiveness of Public Participation in Urban and Rural Grassroots Governance Based on a Big Data Platform

Abstract Urban and rural grassroots governance is the cornerstone of the modernization of national governance, and strengthening effective public participation is also the continuation and sublimation of the strategy of poverty alleviation. This paper utilizes gene structure for expression, constructs the RMUGS model, and then constructs a three-level retrieval algorithm based on the various levels of the model to analyze big data on the results achieved by public participation in urban-rural grassroots governance in Z town of Guangdong province. Town Z has been able to achieve good results in urban-rural grassroots governance, as shown in the data analysis results. Significant results have been achieved in the improvement of infrastructure, with the overall degree of improvement reaching more than 75%, and the highest improvement of public lighting reaching 90% of the degree of improvement. The number of people actively participating has gone up from 60.33% in 2020 to 77.95% in 2022, with general residents without positions experiencing the greatest increase. The above data analyzed by the RMUGS model clearly shows that public participation in urban and rural grassroots governance has a very high degree of effectiveness, which provides effective directions and ideas for future grassroots governance.

Characterization of Red Civic Education in Colleges and Universities under Big Data Platform and Optimization of Teaching Methods

Abstract In the era of big data, the implementation of red ideological and political education has a positive and practical guide to the daily behavior habits of college students, and plays a positive guiding role. The lack of red ideological and political education is one of the main reasons for the abnormal behavior of college students. In this paper, the daily behavioral habits of college students are modeled, and according to the different distribution characteristics of individuals on campus activities, abnormal class determination and cluster analysis are carried out. The abnormality detection operator is used to detect the strange behavior of college students. In the study of abnormal behaviors of college students, the proportion of medium and low intensity of deviant behavior college students in sports behavior is as high as 73%, the proportion of high intensity in dietary behavior is 53%, and the proportion of high intensity in consumption behavior is also more than 50%. The psychological attention characteristic value of abnormal behavior college students is lower than that of average behavior college students, and the fluctuation is slightly more significant than that of intermediate behavior college students. Except for the forgiveness and modesty factors, the other 13 positive psychological quality factors of abnormal behavior college students are lower than those of average behavior college students, and the evaluation of the quality of political thinking is more inadequate than 80 points, which is in the range of poor and general assessment. Given the abnormal behavior of college students, the optimization direction of red Civics teaching method is proposed.

Modular Application of Evaluation System for Innovation and Entrepreneurship Education of College Students--Based on Big Data Platform Analysis

Abstract Based on the objectives of innovation and entrepreneurship education for college students, this paper constructs the evaluation index system, utilizes the improved Apriori algorithm to mine the association rules between the indicators, and reflects the relevant influence between the evaluation indicators through the association rules with higher support and confidence. Meanwhile, based on the big data platform, an evaluation system for innovation and entrepreneurship education for college students is established, and the level of innovation and entrepreneurship education for students is evaluated. The analysis of the correlation rules between the evaluation indicators found that the support degree of the rule that students with stronger independent ability are better at grasping innovation and entrepreneurship opportunities is 29.97%, which provides a reference for the teachers’ teaching objectives and content development. The results of the empirical analysis of the evaluation system of innovation and entrepreneurship education show that the modular application of the evaluation system created in this paper has a positive effect, and there is a highly significant difference in the evaluation of the “Z13 personal willingness” index before and after the application (P=0.000<0.001). The correlation rule of evaluation indexes in this study lays a theoretical foundation for the effective development of innovation and entrepreneurship teaching, and the modularized application of the innovation and entrepreneurship evaluation system constructed in this paper can improve the level of innovation and entrepreneurship education in colleges and universities to a certain extent.

Practical Reflections on Enhancing Cultural Confidence in English Education in Colleges and Universities under the Big Data Platform

Abstract Day by day, we are realizing the goal of the great rejuvenation of the Chinese nation, which necessitates the development of sufficient cultural self-confidence. The amalgamation of traditional culture and English learning is an essential prerequisite for us to effectively communicate the Chinese story to the world. This paper proposes an optimized ridge regression model with regularization constraints to enhance the preprocessing of cultural data. Subsequently, we propose a statistical model for analyzing the teaching styles of English texts from four perspectives, taking into account the characteristics of English language teaching and the cardinal text statistics algorithm based on word frequency optimization. This model’s analysis revealed that textbook articles reflecting traditional Chinese culture accounted for only 1.8% of all types, resulting in an uneven distribution of grades. Following the implementation of improvements to tackle this issue, 74.86% of the students expressed their belief that enhanced teaching could bolster their cultural confidence. Similarly, 86.6% of the teachers expressed their belief that incorporating Chinese culture into English teaching could enhance students’ cultural awareness, and 90% of the teachers believed it could boost students’ identification with Chinese culture and cultural confidence. It shows that the new teaching method optimized by conducting textual statistical analysis helps to bring into play the role of English education in colleges and universities in enhancing cultural self-confidence.

Guidance and Counseling in Colleges and Universities in Taiwan: Challenges and Perspectives

<p>本文旨在探討臺灣大專校院諮商輔導工作歷經超過60年之發展，輔導工作現況、挑戰與展望。本文採文獻分析法與文件分析，以學生輔導法為依歸，依三級輔導機制、輔導組織定位與資源人力配置，以及績效評估與自我改善機制等三面向探討，並提出如下展望：(1)大專校院諮輔單位之專業形象與組織定位、角色功能宜再定義與明確化；(2)建立校園生態系統合作模式，型塑正向輔導氛圍，共同推動三級輔導工作；(3)建立資料庫或大數據平台，以利掌握現況資訊及趨勢脈動。(4)在績效展現部分，宜結合校務研究建立證據導向與持續改善機制之校本輔導工作特色與品保系統；(5)發展嚴謹、具公信力之諮商輔導認證機制。(6)同時為因應與日俱增之輔導工作需求與挑戰負荷,宜提升區域平台之功能，確保輔導工作品質與效能；(7)網路及後疫情時代宜善用科技，使諮商輔導工作與時俱進。</p> <p> </p><p>This study aimed to explore the development, implications, challenges, and prospects of counseling work at Taiwan’s colleges and universities for more than 60 years. This study employed the literature analysis and document analysis methods to explore the current situation and challenges from three aspects of counseling, namely organizational position and manpower allocation, three levels of guidance and counseling and transition mechanism, and performance evaluation and self-improvement mechanism. It anticipated the following prospects: redefine and clarify the professional image and organizational position and role and function of guidance and counseling units in colleges and universities. Additionally, according to the comprehensive professional definition and by integrating the division of work, a campus ecological system based on a cooperation model should be established, fostering a positive counseling image and collectively promoting the three levels of guidance and counseling. It is recommended that government or academic organizations establish a database or big data platform to acquire current information and trends to expand and improve guidance and counseling work. Regarding performance, it is advisable to incorporate institutional research to establish an evidence-oriented and continuous improvement mechanism for school-based counseling characteristics and quality assurance systems and develop an authentic and rigorous guidance and counseling credential-awarded mechanism. Guidance and counseling professional associations should promote effectiveness evaluation, performance presentation, and counseling work quality assurance system of each school, broadening the implications and scope of colleges and universities’ counseling. Simultaneously, to cope with the ever-increasing needs and challenges of counseling, it is advisable to enhance the functions of the regional platform to ensure the quality and effectiveness of counseling work. Guidance and counseling in the post-epidemic era should be integrated with emerging technologies to develop technological consultation work that can align with current trends.</p> <p> </p>

The application of big data platform in intelligent scheduling of tourism passenger flow based on time window

The application of big data platform in intelligent scheduling of tourism passenger flow based on time window

Information scheduling method of big data platform based on ant colony algorithm

Information scheduling method of big data platform based on ant colony algorithm