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This paper creates a model based on big data, examines higher education's internationalization approach, and simultaneously conducts research. According to the research, the method used in this work has a greater accuracy rate than numerous other well-known methodologies for assessing the risk associated with higher education internationalization strategies, with the maximum accuracy rate reaching 68.9%. It can be seen that the methodology used in this report is more suited for researching the higher education sector's internationalization plan. Information integration management is advancing steadily in the age of big data. To ensure the standardization, informationalization, and scientificization of school education and teaching management, consideration should also be given to the organic integration and full utilization of education internationalization data in the field of higher education internationalization strategy., Competing Interests: The author does not have any possible conflicts of interest., (Copyright © 2022 Minxuan Zhang.)

Ideological and political education (IPE) is aimed at achieving people's free and all-around growth through the use of appropriate methods, and the use of educational methods is integral to the execution of education. Under the influence of big data, it is imperative to strengthen the research on the accuracy of ideology education in colleges and universities (IPECU), which necessitates that ideology educators adopt big data thinking, investigate novel pedagogical approaches, and consistently develop new IPECU conditions. In this paper, a collaborative filtering- (CF-) based algorithm for IPE resource recommendations is presented. Users are given recommendations for educational resources based on their browsing history, browsing patterns, and preferences. The accurate recommendation system can determine users' needs by examining how they use the website in order to suggest more useful information to them. In comparison to the conventional algorithm, the accuracy of the ideological and political education precision recommendation model in this study is 16.75% greater. Teachers can use big data technology to gather students' data information that is dispersed throughout cyberspace, understand students' states in real time, and deliver accurate instructional materials in accordance with students' various states and needs by utilizing the intelligent ideology mode., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Yuefen Wang and Chaoqun Ma.)

Comparative literature and world literature were originally two independent disciplines, but now they are merged into one. In the era of big data, building an efficient information management method is one of the important contents of university reform. Based on the construction of comparative literature and world literature, this paper applies the DSS (Decision Support System) structure based on data warehouse to the construction of comparative literature and world literature, and constructs the DSS structure of comparative literature and world literature. The related knowledge of DM (data mining) is used for research, and the application design and implementation process of DM are introduced into the subject quality evaluation system. The research shows that the experimental results of NB (Naive Bayes) algorithm will not take too long, about 56.08 s, and the compression ratio is 0.897, when the parameters are basically the same. This model can effectively help schools to analyze data and make decisions, and improve the level of information construction in schools., Competing Interests: The author does not have any possible conflicts of interest., (Copyright © 2022 Wen Xu.)

We provide a brief overview of the connotation and characteristics of data mining technology in the era of big data, analyze the feasibility of data mining technology in business management from the economic and technical perspectives, and propose specific application suggestions according to the content and requirements of business management. This paper describes in detail the principles and steps of using the weighted plain Bayesian algorithm and the decision tree algorithm to analyze students' performance; firstly, we need to obtain the plain Bayesian analysis model of college students' learning literacy in physical education and the C4.5 graduation literacy analysis model, and then use certain rules to combine the weighted plain Bayesian algorithm and the decision tree algorithm to obtain the WNB-C4.5 college students' learning literacy analysis model. In addition, in the prediction of financial risks, the classification scheme can be used in the judgment of violation of regulations, but the most used classification scheme is the decision tree. Experiments show that the effectiveness of this scheme in data mining for financial companies is increased by 2% compared to the benchmark method., Competing Interests: The author declares that they have no conflicts of interest regarding this work., (Copyright © 2022 Jinfeng Cheng.)

With the economic development, smart communities have been widely studied and applied. However, the system in this field is not perfect, and there are still a series of problems, such as high construction cost, low level of intelligence, mutual independence of different systems, difficulty in unified management, and so on. To solve the above problems, this paper proposes the smart community big data dynamic analysis model based on logistic regression model. First, this paper constructs the big data research architecture of smart community based on IOT technology, including IAAs, DAAS, PAAS, and SaaS layers and the virtual service layer of resource scheduling of spatiotemporal information cloud platform optimized by spatiotemporal law. And the IoT platform is designed to collect data to lay the foundation for research. Second, this paper is oriented to the big data application requirements with distribution and mobility as the main technical characteristics. Based on the distributed data flow, this paper designs mining operator to provide technical support for the data mining algorithm; at the same time, this paper constructs a high-dimensional random matrix model for measuring big data and then deduces its abnormal data detection theory and method to detect high-dimensional abnormal data. Finally, this paper uses logistic regression model to predict the development trend of smart community and provide guarantee for smart community service. The simulation results show the efficiency and accuracy of prediction can be improved based on logistic regression model. Furthermore, it effectively avoid repeated construction and waste of resources in the community and form a new community management model based on intelligent and information-based social management and service., Competing Interests: The author declares that there are no conflicts of interest., (Copyright © 2022 Hong Jiang.)

This paper presents a big data analysis and prediction system based on convolutional neural networks. Continuous template matching technology is used to analyze the distributed data structure of big data, and the information fusion processing of cloud service combination big data is combined with matching related detection methods, frequent item detection, and association rule feature extraction of high-dimensional fusion data. A clustering method is adopted to realize the classification and mining of cloud service portfolio big data. The hardware equipment of the car to detect the surrounding environment is complicated, and the combination of the convolutional neural network and the camera to detect the surrounding environment has become a research hotspot. However, simply using the convolutional neural network to process the camera data to control the turning angle of the car has the problems of long training time and low accuracy. An improved convolutional neural network is proposed. The experimental results show that the accuracy of data mining by this method is 12.43% and 21.76% higher than that of traditional methods, and the number of iteration steps is shorter, indicating that the timeliness of mining is higher. This network structure can effectively improve the training speed of the network and improve the accuracy of the network. It is proven that the convolutional neural network has faster training speed and higher accuracy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Xuegong Du et al.)

As an educational concept based on learning output, OBE (Outcome-Based Education) is student-centered and emphasizes students' personal progress and learning achievement. Based on BD (big data) analysis, this paper proposes a new talent training mode for the electronic information science and technology specialty. This model employs BD analysis technology to examine the correlation index between social demand and talent cultivation, based on the employment situation of students in the country in previous years. The teaching reform was carried out under the OBE concept, and a new training scheme was formed. Training objectives, graduation requirements, curriculum system, and continuous improvement mechanism were all determined. This paper proposes an algorithm for determining the degree of similarity between the knowledge required by the organization and the knowledge held by its employees. The person with the highest similarity is identified as the training candidate by the algorithm, and the training candidate is then trained according to the knowledge that the organization requires. Talent training in the field of electronic information science and technology has yielded positive results., Competing Interests: The author declares that there are no conflicts of interest., (Copyright © 2022 Jifeng Liang.)

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company's accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company's accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Jifan Chen and Muhammad Talha.)

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages., Competing Interests: The author declares no conflicts of interest., (Copyright © 2021 Haiqiu Li.)

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments. [ABSTRACT FROM AUTHOR]

Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalisation, global economic and trade activities are escalating. However, environmental problems and the emergence of green economy, a response to these problems, has led to the widespread introduction of green trade barriers. These barriers implicitly limit the development of trade activities. This paper focuses on the export difficulties caused by green trade barriers and proposes a method to quantify discrete product characteristics, explore the internal characteristics of commodities and decide optimally on intended export regions. Firstly, the discrete feature of products is quantified by quantitative transformation method. Secondly, the quantitative data are used to derive the best decision for export regions through support vector regression (SVR) method. Particle swarm optimisation is used in optimising SVR parameters to achieve high-precision decision making. Comparison with historical data from the industry park shows the identification accuracy of the optimised SVR model to be better than that of the traditional regression model. This finding presents a novel perspective for developing import and export under the background of green trade barriers. [ABSTRACT FROM AUTHOR]

During the synchronous online math competition, a high volume of data is continuously generated and collected from the user at every event. Those valuable data can be processed and analyzed to support many decisions in a second such as user state, cheating detection, online help desk, etc. In addition, enormous demands from different roles accessing those data, such as data analysts, data scientists, and executives, have increased recently. This paper presents a new architecture for synchronous real-time data streaming for online testing. Also, this paper identifies three challenges that need to be addressed: First, most online testing organizations rely on open-source frameworks for big data processing and streaming - Hadoop and Kafka. Second, we add the serverless architecture for synchronous real-time data streaming to an open-source learning management system, Moodle, to meet the synchronized online test requirement. Third, we discuss the benefit of the architecture in terms of high availability, cost, and technologies. [ABSTRACT FROM AUTHOR]

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi-node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi-node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low-priority data streams because the delay of high-priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements. [ABSTRACT FROM AUTHOR]

The Special Issue on "Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process" received several state-of-the-art research papers related to the application of big data analysis and advanced analytics in three different areas of sustainable production processes: prognostics and health management (PHM), supply chain management (SCM), and safety policy in the workplace. On a similar subject, in the paper "A Time-Series Data Generation Method to Predict Remaining Useful Life" [[5]], a time series generation method, which is useful when there are not enough fault-labeled data available, is presented by Ahn, Yun, and Hur. We live in the big data era, in which a large amount of information is continuously created, registering all kinds of events, such as those generated in the design, planning, control, and execution of manufacturing, logistics, and supply chain processes. [Extracted from the article]

Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis. [ABSTRACT FROM AUTHOR]

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the predictive effect of artificial intelligence on user demand in big data social media and to provide suggestions for developing enterprise innovation frameworks and implementing marketing strategies. In response to the inconsistency between the supply of enterprise products and services and market demand, deep learning algorithms have been introduced using social media big data analysis. This algorithm has been improved to construct a user demand prediction model in social media big data based on bidirectional long short-term memory (BiLSTM) fused with Word2Vec. The model uses data acquisition and pre-processing, Word2Vec algorithm to vectorization the data information, and BiLSTM network to model and train the sequence. Finally, the model is evaluated as an example. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students' data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods: The study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results: The paper offers insights into how the teaching strategy effectively enhances students' data analysis and critical thinking skills. It investigates the specific impact of active learning on students' engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion: The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session's effectiveness to accumulate in students' personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era. [ABSTRACT FROM AUTHOR]

Energy-efficient and sustainable building management has always been a key concern surrounding buildings. The rise of environmental and social concern in today's world has brought more attention to the issue of sustainable and smart building management. This paper aims to review the state-of-the-art research and performance on building management that aims to make more sustainable and energy-efficient decisions. This paper classifies building management based on technologies utilized for management and different aspects of management that should be considered when regarding the larger picture of "sustainability". Additionally, while keeping in mind that long-term sustainability cannot be achieved through energy management alone, this research investigates previous works that also mention diverse aspects that must be taken into consideration when creating a truly successful smart building environment: costs, occupant comfort, and security. Of course, each field deserves an extensive analysis, but the purpose of this review paper is to deliver current research that has brought attention to the rapidly shifting and developing field of smart buildings to provide a macro-level holistic viewpoint on how smart buildings and homes should be approached from a sustainability viewpoint. [ABSTRACT FROM AUTHOR]

Quantile regression has been widely used in many fields because of its robustness and comprehensiveness. However, it remains challenging to perform the quantile regression (QR) of streaming data by a conventional methods, as they are all based on the assumption that the memory can fit all the data. To address this issue, this paper proposes a Bayesian QR approach for streaming data, in which the posterior distribution was updated by utilizing the aggregated statistics of current and historical data. In addition, theoretical results are presented to confirm that the streaming posterior distribution is theoretically equivalent to the orcale posterior distribution calculated using the entire dataset together. Moreover, we provide an algorithmic procedure for the proposed method. The algorithm shows that our proposed method only needs to store the parameters of historical posterior distribution of streaming data. Thus, it is computationally simple and not storage-intensive. Both simulations and real data analysis are conducted to illustrate the good performance of the proposed method [ABSTRACT FROM AUTHOR]

In the context of the continuous promotion of China's big data development strategy, this paper quantitively analyses China's existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China's big data policies. This paper carries out a quantitative textual analysis of China's national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies. [ABSTRACT FROM AUTHOR]

During the Ming Dynasty, China and Korea exchanged frequently and recorded a large amount of written information, which is of great value for understanding the culture of that time. The large amount of data makes it difficult to conduct quantitative analysis by researchers, which makes the analysis limited. This paper carries out a research on the cognition and analysis method of Chinese and Korean envoys to foreign cultures in Ming Dynasty based on big data analysis technology. Based on the literature research, this paper determines the ontology model establishment method to efficiently detect the written records of Chinese and Korean envoys in Ming Dynasty. The established ontology model and the improved clustering analysis method can improve the efficiency of data detection, reduce the error of data detection, and provide data basis for the research of this paper. According to the technology of big data analysis, this paper analyzes the focus and status class of Chinese and Korean envoys in Ming Dynasty and analyzes cognition of the Chinese and Korean envoys for the foreign culture. The results show that the envoys of the Chinese and Korean pay different attention to the foreign culture due to their different cognition of the foreign culture, compared with Ming Dynasty envoys, Korean envoys paid 15.3 percent less attention to geography, 19.7 percent more to history, 11.7 percent more to people, and 16 percent less to customs. This reflects the two envoys' different perceptions of the foreign culture. And the status class of the envoys exacerbates this difference. In the early Ming Dynasty, the creative diversity of Ming envoys was far lower than that of Korean envoys. As time went by, the creative diversity of Ming envoys increased. The results provide support for further understanding of Chinese and Korean culture and their relationship in Ming Dynasty. [ABSTRACT FROM AUTHOR]

The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior. [ABSTRACT FROM AUTHOR]

Based on virtual reality technology, landscape 3D modeling provides users with the possibility to construct a simulated garden landscape environment design effect online, so it has high requirements for accuracy. With the continuous improvement of precision requirements, the number of people involved in the construction of 3D models is also increasing, which puts forward higher requirements for modeling. Based on this, this paper studies the optimization strategy of landscape space 3D model based on big data analysis. Based on the analysis of the establishment of the 3D model and the related algorithm research, this paper analyzes the optimal design of the 3D model under the background of big data. In the 3D modeling of the edge folded area, it is based on the traditional quadratic error measurement grid simplification algorithm, combined with the vertex error matrix to simplify, so as to shorten the modeling time. Based on an efficient search algorithm, an adaptive nonsearch fractal image compression and decoding method is proposed in the image compression and decoding stage of 3D modeling. The search is performed by specifying the defined area block. Finally, an experiment is designed to analyze the performance of the optimization algorithm. The results show that the improved edge folding region algorithm can reduce errors on the basis of ensuring image quality, and the adaptive search algorithm can shorten the search time and improve the compression rate. This method provides a technical reference for the visualization experience and simulation system of garden landscape design and improves the presentation quality of virtual garden landscape design scenes. [ABSTRACT FROM AUTHOR]

Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed. [ABSTRACT FROM AUTHOR]

A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

In view of the limitations of the current research on students' single attribute psychological problems. In this paper, a multiattribute social network model was constructed based on students' social data and psychological label data, and the improved MANE algorithm was used to solve the problem to predict students' psychological problems. In addition, DeepWalk and Node2vec network embedding algorithms were used to embed students' multiattribute social network, respectively, so to verify the effectiveness of the model. Finally, based on the prediction model of students' psychological problems, this paper uses Django web framework, MySQL database, and Bootstrap framework to design a personality prediction system, including data storage, algorithm training and prediction, result display, and other modules. [ABSTRACT FROM AUTHOR]

With the development of big data concept and technology, big data has an important impact on human development. This paper studies the relationship between the consumption pattern and mental health of enterprise employees under the normalization of epidemic prevention and control. Starting from the consumption structure and behavior of enterprise employees, it defines the meaning of enterprise employees' consumption and the connotation of enterprise employees' health psychology and analyzes the relationship between consumption behavior and consumption psychology and the elements of enterprise employees' health psychology. Based on the change of employees' income structure and consumption patterns, this paper speculates the relationship between employees' consumption patterns and mental health, analyzes the correlation between employees' consumption patterns and mental health through a questionnaire survey, and calculates the Correlation Clustering statistical results. It plays an important role in building a good enterprise staff consumption culture under the normalization of epidemic prevention and control and effectively realizes the significance of purifying the social consumption environment. [ABSTRACT FROM AUTHOR]

Introduction. A review of the literature on data literacy from 2018 to mid-2023. Method. A scoping review of the data literacy literature, in order to provide an overview of a topic, clarify key concepts, and identifying knowledge gaps. Analysis. A detailed search strategy was applied to three data bases: SCOPUS, the ACM Digital Library, and the IEEE Xplore, followed by a qualitative analysis using a coding matrix of eight categories. Results. Trends and gaps in the research were identified. The volume of research on data literacy is growing. Most data literacy research is in the context of formal learning environments. The focus of data literacy tends to be on digital data, interactions with big data systems, and the datafication of life. Critical data literacy approaches were identified in one third of the sample papers - a small but emerging stance to data literacy education. Most empirical studies about data literacy occurred in the Global North. Conclusion. This paper contributes to our understanding of how data and data literacy are currently interpreted in the research literature, as well as the types of topics, skills, and data practices explored. It reveals developing trajectories and proposes future steps for building out the field of data literacy. [ABSTRACT FROM AUTHOR]

The amount of data in the maritime domain is rapidly increasing due to the increase in devices that can collect marine information, such as sensors, buoys, ships, and satellites. Maritime data is growing at an unprecedented rate, with terabytes of marine data being collected every month and petabytes of data already being made public. Heterogeneous marine data collected through various devices can be used in various fields such as environmental protection, defect prediction, transportation route optimization, and energy efficiency. However, it is difficult to manage vessel related data due to high heterogeneity of such marine big data. Additionally, due to the high heterogeneity of these data sources and some of the challenges associated with big data, such applications are still underdeveloped and fragmented. In this paper, we propose the Vessel Data Lakehouse architecture consisting of the Vessel Data Lake layer that can handle marine big data, the Vessel Data Warehouse layer that supports marine big data processing and AI, and the Vessel Application Services layer that supports marine application services. Our proposed a Vessel Data Lakehouse that can efficiently manage heterogeneous vessel related data. It can be integrated and managed at low cost by structuring various types of heterogeneous data using an open source-based big data framework. In addition, various types of vessel big data stored in the Data Lakehouse can be directly utilized in various types of vessel analysis services. In this paper, we present an actual use case of a vessel analysis service in a Vessel Data Lakehouse by using AIS data in Busan area. [ABSTRACT FROM AUTHOR]

A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper. [ABSTRACT FROM AUTHOR]

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human's daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya's buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport. [ABSTRACT FROM AUTHOR]

The stock market is a real and continuously evolving extremely complex dynamic system. This paper analyzes the change of stock market efficiency from the perspective of dynamic evolution, and the recursive graph method is used to obtain the dynamic characteristics of stock price time series. For the sharp rise and fall of stock prices, this paper uses the heuristic segmentation algorithm of nonlinear time series mutation detection to study the detection of market dynamics characteristics before the stock market crash. Based on the above research results, this paper studies the dynamic evolution of financial markets and the construction of a complex network of dynamic characteristics between financial markets. The simulation results show that there are typical characteristics of small world network in stock market complex network, the stock market complex network shows stronger synchronization ability, and the transmission range of information between the stock markets in the complex network is significantly expanded. [ABSTRACT FROM AUTHOR]

With the development of sports and information technology, people use mathematical tools and computer technology to study sports data and mine the intrinsic value of sports data. Statistical methods are the most widely used to achieve this goal. The research purpose of sports effect evaluation research is to understand the impact of sports on physical fitness through mining and analysis of sports data and to provide theoretical guidance for the public to participate in fitness activities scientifically and effectively. At present, in the study of combining individual performance test data, the research on the standardization of physical fitness monitoring data for sports training is relatively scarce. Therefore, based on the background of big data, this paper integrates the existing data standardization work and designs a plan for the standardization of physical fitness monitoring data for sports training. Combined with machine learning, data preprocessing is performed to obtain the data required by the machine model. The comprehensive physical fitness rating model and the recommendation model are established to realize the development of physical fitness monitoring service applications. In the experiment, compared with the three classical methods, the results show that the classification accuracy of this paper is 4% higher than that of other algorithms, which can more intuitively reflect the characteristic samples of sports training. In this paper, the data mining and analysis technology based on feature indicators in the mining and application of sports data has great application value for human fitness guidance and has certain research value and market application prospects. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of tourism management classroom teaching, this article intelligentizes the teaching process of tourism management, and combines multimedia technology and big data technology to improve the teaching process. The crisis early warning system based on the image monitoring index system monitors and observes the components of the tourist destination from the perspective of tourists, and calculate the indexes based on the monitored results, so as to discover the possibility of image crisis events in time and better serve tourists. In addition, this article builds a tourism management classroom teaching system based on multimedia and big data technology on this basis, and tests the system. Through research, it can be seen that the crisis early warning system based on the image monitoring index system and the tourism management classroom teaching system based on multimedia and big data technology proposed in this paper have good practical effects. [ABSTRACT FROM AUTHOR]

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis performed using TextBlob, showed that in the responses where participants indicated their biggest concern related to COVID-19, the average subjectivity by the age group of 41–50 decreased from April 2020 to June 2020, the average subjectivity by the age group of 71–80 drastically increased from May 2020, and the age group of 11–20 indicated the least level of subjectivity between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11–20, 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, and 81–90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11–20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21–30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31–40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41–50, 51–60, 61–70, 71–80, and 81–90. Fifth, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The design of the deep learning model comprised 8 neurons for the input layer with the ReLU activation function, the ReLU activation function for all the hidden layers with 12 neurons each, and the sigmoid activation function for the output layer with 1 neuron. The model utilized the responses to multiple questions in the context of fear and preparedness related to COVID-19 from the dataset and achieved an accuracy of 91.62% after 500 epochs. Finally, two comparative studies with prior works in this field are presented to highlight the novelty and scientific contributions of this research work. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

In road transport, most vehicles today still rely on internal combustion engines. However, these engines have lower efficiency and generate higher pollution levels compared to electric motors. Consequently, there is a growing interest in the transition from conventional vehicles to electric ones. However, the transition to an electrified road transport system is not without challenges. Among these, the impact that electric vehicle charging will have on the electricity grid is of particular concern. This paper analyzes different charging scenarios for plug-in hybrid electric vehicles (PHEVs) and proposes charging strategies to minimize their impact on the electricity grid. The analysis is based on a large dataset of trips in urban areas in Italy. The study shows that smart charging of PHEVs can be implemented to minimize the impact on the electricity grid. The implementation of optimized charging strategies can contribute to making PHEVs a valid, eco-sustainable alternative to conventional vehicles while also promoting the stability and efficiency of the electricity grid. The study aims to verify the effectiveness and efficiency of the flexible charging strategy by comparing the common charging operation (first in–first out) with other, less impactful charging schemes. [ABSTRACT FROM AUTHOR]

The occurrence of 'sensing holes' not only hinders seamless data collection but also leads to misinterpretation of information in certain areas under extensive data analysis. In order to overcome this, various sensor relocation strategies have been proposed, but the existing relocation strategies revealed problems such as the ping-pong, shaded area, network disconnection, etc. This paper conducted research on relocation protocols in a distributed environment that is very suitable for real-world situations and efficiently recovering the problem of sensing holes. First, a simulation was performed on the distribution of the shaded area for data collection, which is a problem with the existing representative relocation protocol. After that, a data collection capability was newly added to the relay node, which had been in charge of only communication between cluster zones so far, and with this additional functionality, the performance of the revised sensor relocation algorithm was dramatically improved to overcome the existing problems. In addition, the performance and validity of the proposed algorithm were verified through various simulations. [ABSTRACT FROM AUTHOR]

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The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., "neutral" sentiment was present in most of the Tweets. It was followed by "negative" and "positive" sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset. [ABSTRACT FROM AUTHOR]

In order to promote the development of agro-ecological economy and improve the reliability of the development model of agricultural circular economy, this paper explores the general framework of the coordinated development of the agricultural logistics ecosystem from the perspective of the construction of the agricultural logistics ecosystem, the composition of the ecosystem, the synergy stage, and the synergy goal. Moreover, this paper combines big data and Internet of Things technology to analyze its own path of the cooperative development mechanism of the agricultural logistics ecosystem, and uses typical cases to show the internal relationships between the main bodies of the agricultural logistics ecosystem. In addition, this paper attempts to explain the core essence of the agricultural logistics ecosystem and the internal relations of the subjects through the practice of enterprises. After constructing the system framework, this paper combines the intelligent algorithm to construct the intelligent model structure, and analyzes the system function realisation process based on the actual situation. Finally, this paper evaluates the performance of the algorithm model constructed in this paper through experimental research. From the experimental statistical results, it can be seen that the algorithm model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, with corresponding effective data-cleaning strategies being researched. This paper begins with a comprehensive and systematic review of studies related to DQ. On the one hand, we classify these DQ-related studies into six types: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. On the other hand, we discuss the corresponding data-cleaning strategies for each DQ type. Secondly, we examine DQ issues and potential solutions for a public bus transportation system, utilizing a real-world traffic big data platform. Finally, we provide two representative examples, noise filtering and filling missing values, to demonstrate the DQ improvement practice. The experimental results show that: (1) The GPS noise filtering solution we proposed surpasses the baseline and achieves an accuracy of 97%; (2) The multi-source data fusion method can achieve a 100% missing repair rate (MRR) for bus arrival and departure. The average relative error (ARE) of bus arrival and departure times at stations is less than 1%, and the correlation coefficient (R) is also close to 1. Our research can offer guidance and lessons for enhancing data governance and quality improvement in the bus transportation system. [ABSTRACT FROM AUTHOR]

Exoskeletons have emerged as a vital technology in the last decade and a half, with diverse use cases in different domains. Even though several works related to the analysis of Tweets about emerging technologies exist, none of those works have focused on the analysis of Tweets about exoskeletons. The work of this paper aims to address this research gap by presenting multiple novel findings from a comprehensive analysis of about 150,000 Tweets about exoskeletons posted between May 2017 and May 2023. First, findings from temporal analysis of these Tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of Tweets, the lowest number of Tweets, Tweets with the highest number of hashtags, and Tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of Tweets posted per hour and the different characteristics of these Tweets. Third, the paper presents a multiple linear regression model to predict the number of Tweets posted per hour in terms of these characteristics of Tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech, #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these Tweets was performed, and the results show that the percentages of positive, neutral, and negative Tweets were 46.8%, 33.1%, and 20.1%, respectively. To add to this, in the Tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by sentiments of joy, disgust, sadness, fear, and anger, respectively. Furthermore, hashtag-specific sentiment analysis revealed several novel insights. For instance, for almost all the months in 2022, the usage of #ai in Tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, lexicon-based approaches were used to detect possibly sarcastic Tweets and Tweets that contained news, and the results are presented. Finally, a comparison of positive Tweets, negative Tweets, neutral Tweets, possibly sarcastic Tweets, and Tweets that contained news is presented in terms of the different characteristic properties of these Tweets. The findings reveal multiple novel insights related to the similarities, variations, and trends of character count, hashtag usage, and user mentions in such Tweets during this time range. [ABSTRACT FROM AUTHOR]

In the last decade and a half, the world has experienced outbreaks of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika virus, Middle East Respiratory Syndrome (MERS), measles, and West Nile virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly, as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this area of research have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak that were posted on Twitter between 7 May 2022 and 3 March 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes—Views and Perspectives about Mpox, Updates on Cases and Investigations about Mpox, Mpox and the LGBTQIA+ Community, and Mpox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was Views and Perspectives about Mpox. This was followed by the theme of Mpox and the LGBTQIA+ Community, which was followed by the themes of Mpox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with related studies in this area of research is also presented to highlight the novelty and significance of this research work. [ABSTRACT FROM AUTHOR]

Coronavirus disease-19 (COVID-19), an infectious disease that spreads when people live in close proximity has greatly impacted healthcare systems worldwide. The pandemic has so disrupted human life economically and socially that the scientific community has been impelled to devise a solution that assists in the diagnosis, prevention and outbreak prediction of COVID-19. This has generated an enormous quantum of unstructured data that cannot be processed by traditional methods. To alleviate COVID-19 threat and to process these unstructured data, big data analytics can be used. The main objective of this paper is to present a multidimensional survey on open source datasets, techniques and tools in big data to fight COVID-19. To this end, state-of-the-art articles have been analyzed, qualitatively and quantitatively, to put together a body of work in the prediction of COVID-19. The findings of this review show that machine learning classification algorithms in big data analytics helps design a predictive model for COVID-19 using the open source datasets. This survey may serve as a starting point to enhance the research in COVID-19. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale, capacity, and coverage of the modern transmission network, the role of the power system in the national economy is increasingly prominent, and the disconnection of the power system will have a huge impact on society and people's lives. Due to the long transport distance and wide coverage of transmission lines, natural conditions and human factors have caused great difficulties in line operation and maintenance. How to effectively improve the operation and maintenance of transmission lines to ensure the stability and safety of the power grid has become a common problem for the power industry and scientific researchers to discuss. In recent years, the information society has stepped into the era of big data, and big data has developed rapidly, becoming a hot area favored by academia and industry, and is widely used. Through big data analysis, potential operation rules can be discovered from a large amount of grid information, providing maintenance personnel with corresponding maintenance decision support. Using big data technology for transmission line fault analysis can effectively reduce accident processing time and avoid accident expansion. Therefore, this paper combines the underground transmission line fault of power grid with the fuzzy KNN algorithm model to apply the underground transmission line state intelligent monitoring system and conducts the study of real-time data collection and fault diagnosis analysis of the underground transmission line fault of power grid, and this paper conducts the transmission line fault analysis experiment, which fully confirms the feasibility and effectiveness of the algorithm model proposed in the paper and concludes that the data analysis model proposed in this paper. The proposed data analysis model has good innovation and practical feasibility. [ABSTRACT FROM AUTHOR]