Big Data Analysis Methods Research Articles

A semantic big data analysis method based on enhanced neural networks in IoT

AbstractDue to the growth of neural networks, the semantic big data analysis method can classify images at the pixel level, which is very suitable for the needs of IoT. In semantic big data analysis methods, the DeepLab algorithm is an improved and highly accurate algorithm based on enhanced neural networks. However, the DeepLab algorithm does not fully utilize global information, resulting in poor performance for complex scenes. Therefore, this article makes improvements by introducing a global context information module and providing prior information of complex scenes in images. It extracts global information and merges with original features. It improves the expression ability of features. This global context can enhance the accuracy of semantic big data analysis method, and an attention mechanism is designed. The experimental results display that the improved DeepLab semantic big data analysis method based on self‐attention and global context module has good average pixel accuracy and average intersection to union ratio performance on the Pascal VOC 2012 dataset. And the improvement effect is significant.

A big data analytics method for the evaluation of maritime traffic safety using automatic identification system data

The complex traffic situations are among the factors influencing maritime safety. They can be quantitatively estimated through the analysis of traffic data. This paper explores the impact of complex traffic situations on maritime safety, focusing on inland waterway traffic. It presents a big data analytics method, utilizing data from the Automatic Identification System (AIS) and historical maritime accident records. The methodology involves AIS data preprocessing and spatial autocorrelation models, including Moran's index, to extract and evaluate the dynamic characteristics of maritime traffic. The analysis of traffic characteristic includes a thorough investigation into the spatial-temporal distribution of ship average speed and trajectory density. The paper then introduces an effective traffic characteristic analysis model that evaluates the relationship between maritime traffic patterns and accidents. The study, specifically targeting the Nanjing section of the Yangtze River, reveals variations in ship trajectory density and average speed over time. It identifies several hotspots with a significant local correlation between these factors. Moreover, a substantial correlation is found between the locations of maritime accidents and areas with increased ship trajectory density and average speed. These results may provide insights for traffic safety management and highlight strategies for preventing maritime accidents.

Efficiency evaluation of China's transportation system considering carbon emissions: Evidence from big data analytics methods

China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness.

Analysis on the Influence of Tax Reduction and Fee Reduction Policies on Digital Development Level of Enterprises Based on AHP-DEMATEL

Since structural tax cuts were first incorporated into active fiscal policy in 2008, various tax breaks have been introduced. Based on the detailed enumeration of tax reduction policy measures, this study constructs an evaluation index system for the level of digital development of enterprises in terms of strategy implementation, operation status, technology level, and quality management. Then DEMATEL hierarchical analysis method and big data analysis method are used to analyse and determine the comprehensive influence degree of each index. Finally, this paper analyses the benefits and convenience brought by tax reduction and fee reduction to the digital development of enterprises and puts forward countermeasures and suggestions for the development of digital transformation of enterprises in the context of tax reduction and fee reduction. This study aims to put forward relevant countermeasures for enterprises to enhance their digital transformation and development capabilities.

빅데이터에 나타난 유아 코딩교육에 대한 사회적 인식 변화

Objectives This study uses semantic network analysis, a big data analysis method, to examine the flow of social awareness and demands for early childhood coding education and identify key keywords to improve understanding of the flow of early childhood coding education and to improve early childhood coding education in the future. The purpose is to find implications for the direction of institutional and policy research.
 Methods For this purpose, the entire period, 2007-2023, was divided into a total of 4 quarters such as from September 25, 2007 to September 25, 2011, September 26, 2011 to September 25, 2015, September 26, 2015 to September 25, 2019, September 26, 2019 to September 25, 2023. Afterwards, data was collected using ‘Early childhood coding education’ as a search term in TEXTOM. Next, we conducted emotional vocabulary analysis of TEXTOM based on the collected data. In addition, after two purification processes, a total of 200 keywords, 50 for each quarter, were extracted based on TF-IDF. Finally, CONCOR analysis was performed with the final keywords.
 Results As a result of the study, social interest in early childhood coding education increased over time, content was expanded into various channels, and positive perceptions were consistently higher than negative. In addition, 50 keywords were extracted for each quarter, and as a result of CONCOR analysis, 4 word groups were formed for each quarter. Through 50 keywords and 4 word groups for each quarter, we were able to identify the social awareness and flow of early childhood coding education by quarter.
 Conclusions This study identified the flow of social awareness and demands for early childhood coding education, and based on this, it can be used as basic data to identify people's major interest in early childhood coding educa-tion and prepare policy measures for the development of early childhood coding education.

Understanding the University Student Experience Through Big Data Analytics

Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of student comments is being undertaken. The first and simplest use of big data analytics is for the identification of high frequency keyword groups, which, without big data analytics, would be extremely time consuming. However, the lack of context surrounding keyword groups severely limited the ability to draw meaningful conclusions and highlighted the need for human intervention in the analysis process. Future work includes sentiment analysis. This initial work is an impetus for further exploration of big data analytics methods in qualitative contexts, especially in dynamic contexts where rapid data analysis can form a basis for timely interventions.

Big data analysis method based on time series temperature field data of substation transformer

Transformers are one of the main electrical equipment in the power system, and their operating status directly determines the safety, stability, and reliability of the power supply in the power system. The temperature or temperature rise of a transformer is the most important technical parameter that describes the operating status (accidents, faults, and abnormal operation) of the transformer. The following mainly introduces that the monitoring area is gridded by modeling the three-dimensional reality of the transformer, and then the collected infrared temperature data is accurately mapped into the monitoring grid in three dimensions to form the patrol time sequence temperature big data. Through trend analysis, temperature difference and temperature rise analysis, temperature field gradient analysis and other models, the equipment temperature hidden danger detection focusing on prevention is realized.

Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm

This paper addresses several problems in the power system. Key challenges include low-power information integration, inappropriate system data management, inaccurate system data updating, and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally, we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore, we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time, and realize automatic diagnosis and analysis of faults through operation data.

Directions for using big data analytics in logistics management

Logistics operations are becoming increasingly complex and require accurate data for effective management. The use of big data in logistics management is a relevant issue due to the growing volume of data and the need to optimize delivery and inventory management processes to meet market demands. The purpose of the study was to develop ways to optimize the management of big data analysis in logistics. To achieve this goal, the methods of analysis, experimentation, and comparison were used. As a result of the study, strategies for optimizing logistics management of big data analysis were developed and successfully applied. The Python programming language based programme effectively optimizes delivery routes using a clustering algorithm and visualizes the results of this process. Additionally, an informative diagram has been drawn up to illustrate the key stages of the developed strategies. The study also developed and presented a table describing the use of big data analysis methods in various logistics companies. The companies were compared in terms of functionality, data, results, and field of activity. It is established that the use of machine learning methods and optimization of data storage and processing significantly increases the efficiency of logistics operations. The results of this study can be used by logistics companies of any size, as well as enterprises engaged in supply chain management. In addition, the recommendations and strategies developed in this study may be useful for information technology and data analytics professionals involved in the development of software solutions and systems to optimize logistics processes

Big Data Analysis Methods for Higher Education Physical Education Teaching Interactive Platform and Its Teaching Effectiveness

Abstract The use of big data comprehensively serves physical education, so that students can clarify their learning situation and make reasonable changes in physical education teaching. In this paper, firstly, we do an analysis and investigation on the application of big data in physical education teaching in colleges and universities, apply the tree hierarchy algorithm to collect the key data indexes of physical education teaching classrooms, and use the SMDA aggregation algorithm to aggregate and analyze the collected key data. The weight of each index system of teaching evaluation is calculated using the hierarchical analysis evaluation method. The role and application of analytical methods in physical education teaching are discussed in the context of big data. This paper constructs a system for evaluating teaching effects, which includes 5 primary indicators and 20 secondary indicators. The evaluation results of the 10-factor index system in the teaching process are divided into five grades to obtain the distribution intervals of excellent, good, medium, qualified, and poor students’ learning performance. The results of data analysis of the attributes of this paper show that the percentages of Cluster l, Cluster 2, and Cluster 3 are 22%, 66%, and 12% respectively. The weights of teaching ability, method, content, attitude, and effect in the actual teaching process were 20.97, 24.29, 24.08, 3.89, and 6.31, respectively. This paper elaborates on the effects and roles of four aspects of the interactive platform Big Data application in physical education teaching and learning in the context of Big Data analytics methods. Ideas for the use of big data analysis methods in sports teaching in colleges and universities are presented.

Big data analysis methods and cultural value mining for the historical research of ceramic art

Big data analysis methods and cultural value mining for the historical research of ceramic art

Identifying Intellectual Structure of Geosciences from the Highly Cited Papers

Understanding the intellectual structure of geosciences is crucial for identifying emerging topics, which can guide future research and funding strategies. This study employs Latent Dirichlet Allocation (LDA) topic modeling and co-word network analysis to explore the intellectual landscape of highly cited geoscientific papers. Through empirical analysis, the study identifies 15 research areas, including climate change, geological processes, environmental impacts, and advancements in data analysis and remote sensing. These findings highlight the prominent role of big data analysis and machine learning methods across various geoscientific domains. Additionally, a notable gap has been identified in the integration of these computational methods with research on organic chemistry and formation processes, suggesting a potential direction for future exploration and innovation.

Research on Big Data Analysis Methods Based on Artificial Intelligence Technology

Research on Big Data Analysis Methods Based on Artificial Intelligence Technology

A big data analytics method for the influence of WeChat public platform articles on blood donation behavior

PurposeThe purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.Design/methodology/approachThis paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.FindingsThe results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.Originality/valueFirst, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.

Online Communities as a Form of Organizing Narrative Content about SARS-CoV-2 Vaccination: Big Data Analysis

Distrust of vaccines is one of the key problems of modern healthcare both in Russia and abroad. Mass vaccination against SARS-CoV-2 provoked a large number of social media narratives. Online communities play an important role in the formation and dissemination of narratives. The complex system of numerous online platforms for discussing the topic of vaccination on a social network deserves a special description.
 The purpose of the study is to construct a typology of all significant communities of the popular Russian social network “VKontakte” that publish content on the topic of vaccination against COVID-19 (01/01/2020 – 03/01/2023) and describe it in terms of lobbied narratives. Big data collection and analysis methods were used, as well as narrative analysis. Of the 131,119 communities that published at least one vaccination message during the study period, 691 were selected based on self-positioning criteria (name, description) and/or the number of SARS‑CoV-2 vaccination messages above the established threshold. 14 thematic categories of communities have been identified, the self-positioning of which allows reconstructing narratives about forced vaccination and the right to choose; health care and debate about the benefits/effectiveness of vaccination; conspiracy theories; beneficiaries and trust in the authorities; pseudoscience, vaccination myths and scientific knowledge; childcare and parental rights; contrasting medical science with religious beliefs.
 Narratives formed by online communities signify a pronounced polarization of opinions in the digital society regarding SARS-CoV-2 vaccination. The “pro-waxer” and “anti-vaxxer” narratives are reconstructed. Quantitatively “anti-vaxxer” communities significantly predominate over “pro-waxer” ones.

온라인을 활용한 한국어교육 평가 연구 동향 분석

This paper attempted to examine the part of the evaluation of online Korean language education that took place during the pandemic when non-face-to-face classes were activated and to identify the trends and trends of research at the same time. Research on online Korean language education evaluation began in 2006, when the influx of foreigners was active, and various big data analysis methods were used to draw conclusions and gain insight into educational research trends. 88 academic papers were searched through the RISS (Research Information Sharing Service), and the extracted word parts were limited to nouns, and frequency analysis, centrality analysis (degree centrality, close centrality, betweenness centrality), and topic modeling were conducted to derive results and discuss them.

Research on Urban-Rural Integration and Regional Coordinated Development of Anqing City, Anhui Province in the Context of Digital Economy

In the context of the rapid development of the digital economy, the promotion of the digital urban-rural integration plan is conducive to the modernisation of rural industrial development and the promotion of regional coordinated development. This project is based on the drive of digital urban-rural integration, using the methods of literature analysis, field research and big data analysis to analyse the process, results of urban-rural integration and regional coordinated development. Under the guidance of the spirit of the Twentieth National Congress, it provides some lessons for the existing problems related to urban-rural integration and regional coordinated development, so as to promote urban-rural integration and regional coordinated development.

Моделирование системы мониторинга аграрных экологических систем на основе больших данных

Abstract. Due to population growth and food demand, the monitoring of agrarian ecological systems is becoming increasingly important. This is due to the expected use of resources, increased yields and the impacts of agricultural systems in the face of climate change and increasing anthropogenic pressure. The use of such technologies makes it possible to obtain more accurate and objective data on the state of agricultural ecosystems, which, in turn, is based on decisions made aimed at improving the management of agricultural ecosystems and optimizing agricultural practices. Purpose. In this scientific paper, the purpose is to present the results of the assessment of agricultural ecological systems, developed on the basis of the use of Big Data. Methods. The authors of the article analyze the methods of monitoring agroecosystems and justify a new observation that will improve the quality and control of monitoring results. The main emphasis is placed on the use of big data analysis and machine learning methods to obtain more accurate and objective information about the state of agricultural ecosystems. Scientific novelty. The authors have carried out modeling of monitoring systems for agrarian ecological systems based on big data methodology. This represents a transition from classical approaches to more efficient and accurate ones, which is a significant step forward in this field of research. Results. The new model for monitoring agrarian ecological systems provides opportunities for a more accurate and objective study and assessment of the state of agroecosystems. It also allows you to make informed decisions based on the information received, which is an important guarantee for the sustainable development of the agricultural sector. In conclusion, the authors consider the possibilities for improving efficiency and its application models in various areas of agricultural activity.

Application of Big Data Water Distribution Technology in T Block of Q Oilfield

With the extension of oilfield development time, a large amount of production data has been accumulated in the oilfield water injection development process, which brings great challenges and opportunities to the adjustment analysis, diagnosis, and optimization of oilfield development [1]. Based on geological static data, historical data such as production and monitoring at both ends of injection and production, and using big data and deep learning methods, this paper forms a big data analysis method for water injection optimization with the mode of “finding key layers of key wells, implementing key monitoring and deciding the best scheme”. Good results have been achieved in guiding the adjustment of single well water quantity in Block T of Q Oilfield. Eight water injection adjustment schemes have been deployed on-site, 16 intervals have been adjusted, and the daily water injection has been adjusted from 136 cubic meters to 120 cubic meters. After adjustment, the accumulated oil in the connected oil well stage has increased by 262.3 tons, and the production decline in the well area has been effectively controlled, which provides new technologies and new ideas for the dynamic development adjustment of the oilfield.

Different cultures, different gateways: culture shapes stratified job descriptions on LinkedIn

ABSTRACT Recruiting process serves as an important gateway context to fuel social disparity. Previous studies on social inequality focused on how independent competence valued in high-status contexts, aligns with dominant American cultural norms, while interdependent competence valued in low-status contexts, clashes with these norms. Scholarstheorised that individuals with higher status have more resources to afford independence than those with lower status. However, the reliance on Western samples may overlook the influence of cultural norms, leading to ineffective inclusive human resource development strategies. To explore how societal culture shapes competence models at different levels of status, we utilised big data analysis methods to examine job descriptions across occupational statuses and cultures. We analysed 150,708 online job descriptions posted in China and the U.S.A. on LinkedIn. Our research findings indicate that in the U.S.A, higher-status jobs place greater more emphasize independent competence, whereas in China, higher-status jobs require candidates to possess more interdependent competence. This suggests that culture, rather than material resources, shapes social stratification in the labour market. We discuss these findings’ implications, including the vital role of job descriptions in facilitating or hindering access to desired opportunities and upward social mobility across cultures and how HRD professionals can intervene in different cultural contexts.