Development Of Big Data Technology Research Articles

The Way Forward for Online Public Opinion Governance in the Age of Big Data

With the rapid development of big data technology and the popularization of Internet applications, the role of Internet public opinion in social governance has become increasingly prominent. Internet public opinion not only reflects hot issues of social concern and public sentiment, but also affects social stability and national security. Therefore, strengthening the rule of law governance of online public opinion has become an urgent problem. The purpose of this study is to explore the rule of law basis and major problems of online public opinion governance in the era of big data, to analyses the shortcomings of the current implementation of the rule of law in online public opinion, and to propose corresponding countermeasures. In the context of the big data era, the rule of law in online public opinion governance faces challenges in many aspects. Inadequate supervision, imperfect rule of law system, deficiencies in the implementation mechanism, and weak public awareness of the rule of law together constitute the main problems in the current governance of online public opinion. To effectively deal with these problems, it is necessary to start with strengthening supervision, improving the rule of law system, reinforcing the implementation mechanism and raising awareness of the rule of law. Through these measures, a more mature and effective rule of law system for the governance of online public opinion can be constructed, thereby safeguarding the healthy development of the online information environment and maintaining social harmony and stability.

Exploration of Commercial Banks’ Credit Business in the Context of Big Data

With the development of big data technology, there are many problems in the credit system of commercial banks, commercial banks continue to improve the credit system to better ensure the good development of loan business. Micro, Small, and Medium Enterprises (Msmes) are the foundation of national economy, it is very important to study the financing of msmes, commercial banks should make correct credit decision for different msmes. In this paper, we solve the problem of commercial bank making loan decision to loan enterprise from the source, and study the efficient decision-making method. Based on RFM model, we propose a new YNA model to determine the main value of the studied credit customers, k-means clustering algorithm is used to determine the stratification of certain type of enterprises, KNN model and logistic regression model is used to help banks to identify the risk of msmes. It is found that the use of big data technology to facilitate bank credit decision-making improves the efficiency and accuracy of commercial bank credit business review of different enterprises. Based on the essence of commercial bank credit business model, this paper combines the characteristics of commercial bank credit business and big data through the financing of small and medium-sized enterprises.

Concept and Application of Industrial Heritage Planning Decision in Big Data Era

With the rapid development of big data technology, more and more fields are realizing the importance of big data and applying it to various decision-making processes. Industrial heritage planning decisions are no exception. This article will explore the concept and application of industrial heritage planning and decision-making in the era of big data, aiming to emphasize the importance and application value of big data in industrial heritage planning and decision-making. This article analyzes the development relationship between the construction and transformation of urban industrial utilization and the theory and practice of industrial heritage. Through theoretical and practical technical analysis of specific regions, the value utilization of industrial simulation design has been improved. Through in-depth data mining, the government found that the industrial structure of the region is dominated by traditional manufacturing, lacking support from high-tech industries, and the pollution problem of the surrounding environment is also relatively serious. In response to these issues, corresponding planning plans have been formulated, including measures to introduce high-tech industries, optimize industrial structure, and improve environmental quality. By implementing these measures, the comprehensive strength of the region has been significantly improved and the economic transformation and upgrading have been successfully achieved.

Digital Transformation of Enterprise Human Resource Management Enabled by Big Data

With the continuous development of big data technology, the digital transformation of enterprise human resource management has become a development trend. Human resources is one of the most important resources of enterprises, which is crucial to the competitiveness of enterprises. Enterprises need to attract, retain, and motivate excellent employees, thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market. To maintain advantages in the fierce market competition, enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness. At the same time, this paper analyzes the dilemma faced by enterprise human resource management, points out the new characteristics of enterprise human resource management enabled by big data, and puts forward feasible suggestions for enterprise digital transformation.

Research on the Construction of the 'Fundamentals of Financial Big Data' Course: A Case Study of Wenzhou Polytechnic

With the continuous development of big data technology, the financial sector has raised its expectations for accounting professionals, demanding higher digital capabilities. In response to these talent cultivation requirements, many higher vocational colleges have introduced courses on big data technology. This paper selects the newly introduced course "Fundamentals of Financial Big Data" at Wenzhou Polytechnic as a case study to examine the current state and challenges of the course's construction, and proposes strategies and recommendations. These include accelerating the development of teaching resources; strengthening teacher training and development; deepening teacher-student communication and interaction; and expanding channels of cooperation between schools and enterprises. Course reform should be student-centered, guided by a combination of theory and practice, contextualized within real-case scenarios, and supported by digital resources, to comprehensively enhance the quality of education. The construction of the course should meet the new demands of the big data era, cultivating students' data sensitivity, data analysis skills, and digital innovation thinking, thereby laying a solid foundation for their future professional development.

Big Data Enables the Intelligent Manufacturing Industry

With the rapid development of big data technology, the intelligent manufacturing industry is ushering in unprecedented opportunities. Big data enables intelligent manufacturing, helping to achieve more accurate demand forecasting, production planning and supply chain management, and improve production efficiency and product quality. At the same time, big data analysis can provide real-time monitoring and early warning system, timely find and solve problems in the production process, and reduce operating costs. Through big data technology, intelligent manufacturing enterprises can better grasp consumer needs, optimize product design, provide personalized customized services, and enhance market competitiveness. In the future, big data will continue to play an important role in intelligent manufacturing, helping enterprises to realize digital transformation and upgrade the industrial value chain.

Identifying Land Use Functions in Five New First-Tier Cities Based on Multi-Source Big Data

With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi‘an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management.

An Exploration of Customer Segmentation Methods Based on Clustering Algorithm in the Context of Big Data

Accompanied by the continuous development of big data technology, various industries are well aware of the advantages of big data, which are widely used in customer service work, especially in the support of customer segmentation work, and have achieved good results. In this paper, for the problems of large fluctuation of clustering results and low clustering purity in the traditional data mining process, the big data precision mining technology with improved clustering algorithm is proposed. And it is applied in the field of customer segmentation, and the experimental results show that the improved clustering algorithm is applied in customer segmentation, the result curve fluctuation amplitude is small, and the clustering purity is significantly higher than the traditional algorithm.

Physical Graph-Based Spatiotemporal Fusion Approach for Process Fault Diagnosis.

The rapid development of big data technology and machine learning has increasingly focused attention on fault diagnosis in complex chemical processes. However, data-driven approaches often overlook the inherent physical correlations within the system and lack a robust mechanism for providing trusted explanations for fault diagnosis. To address this challenge, a graph-based fault diagnosis model framework is proposed along with a dependable fault node diagnosis analysis method. In order to enhance the extraction of chemical process features from a spatial perspective, a graph convolution network (GCN)-based node spatial encoding module is integrated. The construction of the adjacency matrix involves combining a priori knowledge of chemical processes with Pearson correlation, thereby incorporating the physical correlations between nodes. Simultaneously, to capture temporal dependencies in fault data, a spatiotemporal feature fusion module based on the long short-term memory network (LSTM) is employed. In terms of model training, a dual-supervision strategy is adopted to ensure stable convergence of the multiclass fault diagnosis model. For model inference, a multi-model voting strategy is designed to mitigate accuracy degradation resulting from model prediction bias. To tackle the interpretability challenge, a fault diagnosis analysis method based on node masking is designed, effectively identifying critical nodes contributing to system faults. Experimental validation on the Tennessee Eastman process demonstrates the effectiveness of the proposed model, achieving high accuracy in fault diagnosis. The average fault diagnosis rate for all fault types reaches 0.9844, showcasing state-of-the-art performance in fault diagnosis.

Application of Big Data Technology in Audit

In the new era of rapid development of big data technology, the three key points of high-quality development of auditing are auditing talents, auditing rule of law and auditing technology. In the traditional audit process, auditors focus on professional judgment and professional competence, make audit plans, implement audit procedures and draw audit conclusions. However, there are many problems in traditional audit work, such as difficulty in obtaining sufficient external information and inefficiency in analyzing audit evidence. Therefore, auditors apply big data technology to audit work to solve various problems and put forward corresponding safeguard measures.

Research on Detection Technology of Abnormal Data in College Physical Education Network Teaching Test Results

In recent years, with the rapid development of big data technology, more and more data are continuously generated with the summary of university systems. Therefore, how to use these educational data to provide more scientific decision-making information for university information builders is very important. This research collected various educational data through the network teaching system and campus information system. After that, the collected data was used to analyze the students' online behavior and to excavate valuable behavioral characteristics. In addition, the experiment also proposed indicators to describe students' behavior, such as network course behavior, network viscosity and life regularity, to provide the basis for the subsequent abnormal performance prediction model. Finally, the experiment used DBSCAN algorithm based on distance optimization for clustering analysis, and constructed a NA model based on multiple classifiers. The research results showed that when, the SC value was 0.711, which was the optimal solution of D-DBSCAN algorithm. At this time, the corresponding number of clusters was 4. When N=2, that was, the base classifier of NA model is composed of C4.5 model and SVM model, the prediction accuracy and time consumption are the most appropriate. The accuracy, recall and F1 values of NA model were 98.16\%, 97.26\% and 0.958 respectively, which was better than that of single model. To sum up, the NA model based on classifiers proposed in the study had higher accuracy and better model performance, can effectively reflect students' academic level, and could provide accurate abnormal performance data for college sports online teaching tests.

A Study of Big Data Prediction in Accounting Management Risks

Abstract With the continuous development of big data technology, the application of big data prediction in enterprise risk management is getting more and more attention. After proposing a framework for applying big data prediction in accounting management risk, this study builds an accounting management risk prediction model based on the optimized particle swarm and random forest algorithms.Company X’s accounting management risk-related data is taken as a research sample, and the effect of the constructed accounting management risk prediction model is investigated by comparing the models and assessing the risk prediction accuracy. The study shows that the PSO-random forest model built in this paper has faster convergence and higher accuracy than the ordinary random forest, and the overall accuracy of accounting management risk prediction is 12% higher. The model’s accuracy in predicting the overall and various types of risks in Company X’s accounting management is more than 85%. The PSO-Random Forest model is a reliable tool for predicting accounting management risks, which is of great practical importance.

A Lightweight and Anonymous Mutual Authentication Scheme for Medical Big Data in Distributed Smart Healthcare Systems.

The rapid development of Big Data technology supports the advancement of many fields like industrial automation, smart healthcare, distributed systems, and many more. Big data is large and heterogeneous data generated from different sources, such as Internet of Things (IoT) devices, weather forecasting, traffic management systems, etc. However, in a distributed smart healthcare industry, unauthorized users or devices can illegally access healthcare Big Data, as well as control the sensor or IoT-enabled devices connected to a patient's body. They can even alter patients' healthcare Big Data by inserting false and misleading data, which may even cause death to the patient. This study presents a lightweight privacy-preserving user authentication scheme to solve the above-said problems in a distributed smart healthcare system. The proposed scheme prevents unauthorized users from getting access to the healthcare system by establishing a secure session for the authorized user. Here, the password protection mechanism allows only a legitimate user to access and modify the patient's healthcare Big Data. The security strength and effectiveness of the proposed authentication scheme is evaluated in this paper, which show that it is more efficient and secure than the state-of-the-art schemes.

DATABASE DEPLOYMENT PATTERN FOR HIGH AVAILABILITY

Ubiquitous digitalization and the development of big data technologies require a high level of availability (24/7) of information resources. Ensuring high availability of the database requires a significant amount of work on the design, testing and maintenance of several components and configurations. High availability can be achieved or maximized for the following deployment options: one database instance (with or without read replicas) and several main ones (with or without a coordinator). High availability ensures the smooth operation of the system by eliminating individual points of failure, while disaster recovery focuses on restoring normal operation after a failure or downtime. There are several deployment options because there is no universal solution. Choosing the best option depends on your specific use case and requirements. This article presents the main concepts of ensuring high availability and the most effective patterns for providing highly accessible database deployments.
 Purpose. definition of effective deployment patterns for database servers to ensure high availability.
 Methodology. The results of foreign and domestic scientific research are used in the work. The author uses theoretical research methods related to the search and analysis of information to identify connections and obtain unique conclusions.
 Results. The most effective deployment schemes for ensuring high availability and fault tolerance of databases are highlighted.
 Practical implications it is advisable to apply the results obtained in the field of DevOps development in order to optimize the process of developing and releasing applications by eliminating a known bottleneck: changes to the database code.

The Role of College Students' Innovation and Entrepreneurship under the New Normal

With the development of big data technology and Internet technology, the "Internet Plus" plays an important role in various fields. It will become a new educational model to propel the development of the innovation and entrepreneurship education of college students, thus providing them with a clear direction of innovation and entrepreneurship, so that they can adapt to the changes of the new environment. At the same time, in the context of the new normal, "Internet Plus" can also bring new ideas to college students' entrepreneurial activities, which can enable them to provide an important contribution to the realization of innovation development goals in China, to cultivate more innovative talents. Each section is developed in a staggered way and connected through the Fengcheng river system to form a circular artery connecting the "Confucian worship" block, Old Street, Daohe Ancient Block and Jing'an Road Block and reaching Wanda Plaza. It aims to realize the perfect combination of tradition and modernity, create a "Confucian worship Tour - Fengcheng River cultural tourism scenic belt" with the characteristics of "Confucianism", "opera", "folk custom" and "business", and promote the establishment of national AAAAA level tourist attraction.

Research on Hierarchical Network Security Situational Awareness Data Fusion Method in Big Data Environment

Abstract With the globalization of information and the development of big data technology, people rely more and more on the network. The network brings convenience to everyone at the same time, and many security problems also come. This paper first introduces the NSSA model from different perspectives and analyzes the characteristics of its application, combined with the information fusion to put forward the H-NSSA system model, and constructs the framework of the network security posture assessment. Then, we assess the credibility of the intrusion behaviors in the network, classify and fuse the relevant evidence, and optimize the evidence conflict synthesis rules on this basis to improve the confidence of the event. The Bayesian network model is constructed by describing the causal relationship between elements using the assignment relationship model. Finally, experimental simulation and practical application analysis methods are used to study the network security situation and situation assessment, as well as the practical application of the actual situation in enterprises. The results show that the network suffered a large degree of attack in the 9th-12th time window and a medium degree of attack in the 42nd-43rd time window, and the H-NSSA algorithm model can reach asymptotic convergence in only 68 iterations, and the performance of the algorithm is high.

Big Data in the Book Industry of the PRC: Current State and Development Prospects

The relevance. With the introduction of electronic book publishing and electronic distribution in the book industry, a large amount of both structured and unstructured data is accumulating, the compilation of which has significant potential for the effective development of both individual market entities and the industry as a whole. In the PRC, the issue of implementing Big Data has been studied since 2012. To date, experience has been gained in using this technology that requires generalization.
 The purpose of the scientific research is to analyze the current state and prospects for the development of Big Data technology in the Chinese book industry.
 The methodology. The study was conducted using the method of analyzing.
 The results. The analysis showed that Big Data technology has been actively used in almost all processes of the book industry over the past decade. Its implementation significantly simplifies the management of the industry, individual business entities, content, demand, consumer behavior, thanks to the possibility of forecasting based on reliable data, quickly collected and processed from numerous sources in various formats.
 The scientific topicality. An analysis of the current state and prospects for the introduction of Big Data technology can become a methodological basis for the further transformation of the book industry in the context of digital reality.
 The practical significance. The results of the study can be used in the formation of a strategy for the development of book industry in the context of digital transformation.

A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning.

The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Deep Convolutional Auto-Encoder (DCAE) learned the image-encoded data for representation to obtain features with good discrimination, and the classifier was performed using Deep Neural Network (DNN). The results from five repeated experiments show that the GASF-DCAE-DNN model is overall better in the training (AUC: 0.985 ± 0.001, Gmean: 0.930 ± 0.007, Sensitivity: 0.997 ± 0.003, Specificity: 0.868 ± 0.013) and test sets (AUC: 0.891 ± 0.026, Gmean: 0.830 ± 0.027, Sensitivity: 0.816 ± 0.039, Specificity: 0.845 ± 0.022), with good discriminative power, robustness, and generalization ability. Compared with the best model reported in the literature, the AUC, Gmean, Sensitivity, and Specificity of the GASF-DCAE-DNN model were higher by 23.9%, 27.5%, 39.7%, and 16.2%, respectively, which confirmed the validity and practicability of the model in injury risk prediction. In addition, differences in injury risk patterns between the training and test sets were identified through shapley additivity interpretation. It was also found that the training volume was an essential factor that affected injury risk prediction. The model proposed in this study provides a powerful injury risk prediction tool for future sports injury prevention practice.

The Advantages and Challenges Faced by Business Analytics in the Context of Big Data

In recent years, with the booming development of big data technology, data acquisition and data analysis have become more convenient and efficient, and business data analysis is gradually being widely used in various industries. This paper analyses how business analytics can seize the opportunities for better development in the era of big data, and also explains the challenges faced by business analytics in the era of big data and provides corresponding solutions. The article finds that big data can help business analytics better predict market trends, optimise marketing strategies, provide visual analysis reports, and detect fraud risks; at the same time, the problems of data quality, data security, data processing speed, and data processing capacity arise; finally, the article puts forward ways to solve these problems from the perspectives of improving the quality of data, improving the level of protection, improving the speed of processing, and improving the capacity of analytics, which provides diversified ideas for people.

Slope visualisation and stability study using geographic information science

High-precision visualised three-dimensional (3D) slope modelling and stability analysis have benefited from the quick development of big data technology. This research aims to utilise unmanned aerial vehicle survey data to obtain influencing information on slope topography. A high-precision 3D surface model is established, which is then overlaid with 3D geological models and 3D mineral models constructed using geographic information system technology. This approach enables more efficient and accurate 3D visualisation modelling of slopes. Based on the 3D model, the search for the most dangerous sliding surface under rainfall conditions is completed. The average absolute error of sample points in the surface modelling effect verification experiment is less than 0.5 m, and the average relative error is less than 2%. These results indicate that the overall modelling effect of the 3D slope model can meet the requirements of subsequent stability analysis. According to the results of random simulation, the minimum safety factor on the most neutral surfaces is less than 1, which indicates that the slopes at some locations in this site do have the possibility of landslide. The neutral surface where the lowest safety factor appears can be used to pinpoint the position of the most hazardous sliding surface.