Big Data Analysis Methods Research Articles

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.

Image Processing Algorithm Design of Agricultural Internet of Things Platform Based on Big Data Analysis

Abstract To solve the problems of low signal to noise ratio (SNR), poor processing effect, and long processing times for traditional image processing algorithms, an image processing algorithm for the agricultural Internet of Things platform based on big data analysis was designed. The big data analysis method was used for agricultural low-quality image recognition, and the Internet of Things platform uses sparse representation and a complete dictionary of low-quality image denoising processing with an improved histogram equalization method to enhance the image denoising result and get a high-quality image. Through this, the image processing algorithm design of the agricultural Internet of Things platform was completed. Experimental results show that the algorithm has high SNR and a good image processing effect, and the image processing time is always below 0.7 s, which meets research expectations.

Использование тепловых карт для оптимизации образовательных бизнес процессов в компании Скилбокс

<p>The article addresses data analysis methods for solving the problem of choosing the best solution in order to optimize resources using the example of Skillbox company. The problems of choosing information resources are considered and solved, taking into account market offers. The author considers various methods of data analysis with an emphasis on minimizing the resources required for their processing. In the article, the author describes the practical application of the "Digital Assistant" program, which was developed by the Skillbox company to measure product metrics and analyze the behavior of students. Using the big data analysis methods described by the author, ways to optimize educational business processes that lead to an increase in the profitability of educational courses are considered. Heat maps are considered as one of the main ways to analyze big data, and their practical application is described. The author describes the relationship between business metrics and changes occurring with the educational product and suggests methods and frequency of surveys and feedback processing about the educational path of students.</p>

텍스트마이닝을 활용한 작업관련성 근골격계질환 연구동향 분석

Domestic musculoskeletal disorders have been on the rise since the 1997 IMF crisis, as rapid changes in work environments and practices, such as mechanization, automation, and specialization, have been implemented by various companies to enhance productivity. Furthermore, with increasing awareness and concern among workers, the government has been conducting harmful factor and causal investigations related to musculoskeletal disorders caused by burdensome work tasks through the amendment of Article 39 of the Industrial Safety and Health Act since 2003.
 To ensure the proactive prevention of work-related musculoskeletal disorders and minimize work-related losses for workers' safety, there is a continuous need for systematic and comprehensive research. To address this, the present study conducted a trend analysis to explore new perspectives for preventing industrial accidents.
 The literature collection comprised a total of 653 domestic theses and academic papers from 2003 to 2022 that included the keyword 'musculoskeletal disorder,' gathered through search engines such as the Korean Citation Index (KCI) and the Research Information Sharing Service (RISS). The data were categorized into two periods: the first period from 2003 to 2012 and the second period from 2013 to 2022. Text mining was employed to analyze the frequency (TF) and network connections, which were subsequently visualized.
 The results revealed that the top keywords in work-related musculoskeletal disorder research throughout the entire period (2003 to 2022) were 'work,' 'musculoskeletal disorder,' and 'symptoms,' consistently recognized as essential research topics in both the first and second periods. In the first period, the most frequently discussed factors influencing workers' musculoskeletal disorders included 'posture,' 'physical condition,' 'environment,' 'job stress,' 'time,' 'age,' and the term 'survey' was prominent among the top keywords. In the second period, keywords like 'comparison,' 'development,' 'measurement,' 'method,' and 'investigation' emerged.
 N-Gram analysis indicated that the simultaneous occurrence of the 'musculoskeletal disorder-symptoms' keyword was the most frequent throughout the entire period, followed by high frequencies of 'musculoskeletal disorder,' 'pain,' 'awareness,' and 'burden.' In the first period, body part-related keywords like 'hand-wrist,' 'shoulder-back,' 'wrist-finger,' and 'back-leg' were prominent. In the second period, keywords such as 'workload,' 'safety-health,' 'factor-investigation,' 'risk-assessment,' and 'occupational accident-insurance' were observed.
 This study attempted the first analysis of theses and academic papers on work-related musculoskeletal disorders using text mining, a big data analysis method. The findings contribute to suggesting future research directions on work-related musculoskeletal disorders, holding significant implications for the field.

Application of Financial Big Data Analysis Method Based on Collaborative Filtering Algorithm in Supply Chain Enterprises

At present, the financial situation of China’s supply chain finance is still relatively unstable, and there are still some problems between supply chain enterprises and banks such as asymmetric information, insufficient model innovation and high operational risks. Based on this, this paper proposes and constructs a risk control model of financial big data analysis based on collaborative filtering algorithm. The purpose of this study is to realize the resource integration of supply chain enterprises and optimize the logistics chain, financial chain and information chain through the analysis of financial big data based on collaborative filtering algorithm, provide quality services for supply chain enterprises and good support for solving the financing problems of small and medium-sized enterprises. In order to verify the feasibility of the model, an experimental analysis is carried out. The experimental results show that this model has good scalability and operability, and the algorithm itself also has good scalability. The results of empirical analysis further verify that the design method in this paper has a good recommendation effect in terms of matching degree and user satisfaction. Compared with other risk control models, it is more practical and feasible. This research has certain practical significance for the financial management of supply chain enterprises.

Unfolding the link between big data analytics and supply chain planning

Big data analytics (BDA) has captured growing research interests in operations and supply chain management literature, yet, despite the significant implication, extant knowledge falls short in drawing the link between BDA and supply chain planning (SCP) with in a structured manner. This paper employs the Delphi technique to uncover the synergies between BDA technology, conceptualized as big data sources and BDA methods, and the SCP activities framed with the SCP matrix. The panel runs for three rounds with 35 experts including scholars, supply chain practitioners, and BDA specialists. The results of this paper suggest that the relevance of BDA depends on the focal SCP activity. Thirty-five projections are presented on the expected impact of BDA on SCP that are classified into three groups based on the significance of impact and probability of occurrence. This work advances the understanding of BDA in supply chain management drawing implications to prioritize BDA investment for SCP.

토픽모델링을 이용한 ESG 연구현황 분석

[Purpose]The purpose of this study is to analyze the research trends of ESG research in Korea, focusing on the fields of business administration and accounting, using topic modeling.
 [Methodology]This study focuses on the keyword ‘ESG’ published in domestic academic journals. To this end, as of June 30, 2023, a total of 404 studies of business administration and accounting studies calculated with ‘ESG’ as a search term in the Korea Citation Index are the subject of research. Specifically, morpheme analysis was performed using R, keyword frequency analysis was performed, and frequency analysis and Big-data analysis(data mining, topic modeling) were performed through the extracted keywords.
 [Findings]As a result of this study, five topics were calculated. Specifically, Topic 1 is the role of the environment for management sustainability, Topic 2 is Implications for enhancing corporate value, Topic 3 is Strategy according to economic conditions, Topic 4 is Decision-making at the governance level, Topic 5 is Evaluation and utilization It is a responsibility.
 [Implications]This study is different in that it analyzes research trends using the big data analysis method with the keyword ‘ESG’ limited to business administration and accounting among social sciences. In the meantime, many studies have been conducted in business administration and accounting, but there are not many studies that apply big data methods such as topic modeling. This study has significance in that it goes one step further from the existing research methodology, reduces the subjective opinions of researchers, and identifies macroscopic research trends based on a lot of data through the Big-data methodology.

What Are Statistics: It's Time to set the Record Straight

The authors present their views on the modern concept of statistics as a science, academic discipline, and field of activity. The urgency of the matter lies in an adequate understanding of the essence of statistics, which determines the direction for its further evolution as a tool for increasing the effectiveness of the national socio-economic processes.According to the authors, by the beginning of the XXI century, de jure statistics are associated with activities, measured by data, that reflect a set of phenomena of diverse nature while ignoring de facto activities, associated with an assessment of the quantitative side of mass phenomena as part of information support of cognitive processes and management decision-making in the socio-economic sphere. In the former case, theoretical, methodological, and practical statistical activities are based on the consideration of the statistical population as such, for which is being developed an appropriate toolkit based primarily on the unity of methods of mathematical logic, mathematical statistics, and big data analysis. In the latter case, theoretical, methodological, and practical activities are interpreted within the framework of the concept implying that the quantitative side of mass social phenomena is the object of cognition and management as an objective reality. It is based on the following categories: a statistical indicator, a system of indicators, and an information model.Analysis of the main features of the two types of activity show that their convergence is impossible. The paper concludes that while maintaining the independent status of the first type of activity, called Statistics, it would be appropriate to acknowledge the independent status of the second type of activity, called Socio-economic statistics, as a type of occupation, a specialty and area of training in higher education, a scientific specialty – a branch of science, incorporating it in the professional standard «Statistician», the educational standard of higher education «Socio-economic statistics», and the standard of the scientific specialty «Socio-economic statistics».

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering, crude oil gathering and transportation systems require huge energy consumption and complex structures. It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps. In this paper, the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established. Combined with the big data analysis method, the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems. The comprehensive energy consumption, gas consumption, power consumption, energy utilization rate, heat utilization rate, and power utilization rate of crude oil gathering and transportation systems are predicted. Considering the efficiency and unit consumption index of the crude oil gathering and transportation system, the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method, and the subjective weight is determined by the triangular fuzzy analytic hierarchy process. The entropy weight method determines the objective weight, and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems. Finally, the weak links in energy utilization are identified, and energy conservation and consumption reduction are improved. The above research provides technical support for the green, efficient and intelligent development of crude oil gathering and transportation systems.

Policy Suggestions through Social Perception Analysis of Defense YouTube Channels

YouTube is a platform that delivers video content on diverse topics and enables communication, making it the most utilized service for conveying knowledge and exchanging information. The Korean Army is also proactively operating official Defense YouTube channels related to the military and producing videos to promote a positive perception and instill faith in the military among the general public and soldiers. However, there is a lack of research on the current status of the content and societal perceptions of defense-related channels. To understand societal awareness, we qualitatively analyzed video content and comments. In addition, we used big data analysis methods such as keyword derivation, term frequency analysis, word cloud visualization, word association analysis, and sentiment analysis of comments based on a Korean sentiment dictionary. These analyses identify current defense channel strategies and society's awareness of these strategies and provide a foundation for improving and perfecting future video creation and operation processes.

Molecular pharmacology of coenzyme Q10 in the context of treatment of hyperlipidemic conditions

Background. Therapy of hyperlipidemia with synthetic statin drugs is characterized by a number of side effects. In particular, statin-dependent myopathy occurs as a result of statin inhibition of biosynthesis of cofactor coenzyme Q10 (CoQ10), which plays a central role in the electron transport chains during adenosine triphosphate (ATP) synthesis in mitochondria.Objective: systematic analysis of scientific publications on CoQ10 pharmacology with an emphasis on its effect on the lipid profile.Material and methods. The analysis included 16,788 articles found by “coenzyme Q10 OR ubiquinone" query in the PubMed/MEDLINE biomedical publications database. Topological and metric big data analysis methods were used developed in the scientific school of Academician of the Russian Academy of Sciences Yu.I. Zhuravlev.Results. A much wider range of CoQ10 pharmacological effects was established than simply supporting ATP biosynthesis. Coenzyme Q10 exhibits pronounced lipid-lowering, anti-asthenic, and anti-inflammatory effects and can be successfully used in the treatment of asthenic conditions, cardiovascular pathology (including hyperlipidemia and statin-induced myopathy), carbohydrate metabolism disorders (insulin resistance), and diverse neurological diseases. CoQ10 supplements reduce mortality by 50% in patients with cardiovascular diseases, improve glycemic control in insulin resistance, kidney function, and are useful in the treatment of migraine and neurodegenerative pathologies (Parkinson's disease, etc.).Conclusion. Experimental data and evidence base indicate the potential of using CoQ10 in patients with hyperlipidemia and other cardiovascular system diseases.

Анализ рынка продукции северного оленеводства России на основе анализа массивов больших данных

Reindeer products have become an integral element of the culture of indigenous peoples in the Arctic region, playing a significant role in human adaptation to extreme climatic conditions. This study investigated the online sales offerings of reindeer products to identify the Far North regions in Russia that most actively use information and communication technologies to promote their products in the domestic regional and interregional markets. It also aimed to analyze the range of traditional Arctic food products. The research methodology drew on the theoretical approaches of the spatial economics by A. G. Granberg and the free trade theory by P. R. Krugman. Methods for analyzing big data based on social media data mining were applied. The study utilized messages on the Internet related to the sale of reindeer products in Russia’s Far North regions in 2021–2022. The novelty of this study lies in the implementation of big data collection and analysis methods. It required adapting the PolyAnalyst platform to the needs of a specific research field and improving the analysis methodology. The analysis of online sales offerings revealed the most active Arctic regions: the Murmansk region, the Yamalo-Nenets Autonomous Okrug, the Komi Republic, and the Chukotka Autonomous Okrug. The growing interest in the Arctic has contributed to the formation of a wide range of ready-made dishes and venison processing methods, also becoming a catalyst for growth in Arctic food tourism. This creates important conditions for the economic growth of rural Arctic territories. Online platforms expand opportunities for Arctic ethnic entrepreneurship by overcoming the relative isolation of Arctic economic systems and providing a venue for selling products on the Internet. Consequently, future research prospects involve using big data analysis of the reindeer husbandry market to develop measures for managing the sales of reindeer products.

Analysis of Research Trends in Cosmetology Education using Text Mining

Identifying academic research trends is an essential task to understand the development patterns of how the discipline has changed in the times and social trends based on the accumulated research results so far, and further establish academic identity. This study was conducted according to the following procedure to analyze the trend of Cosmetology Education Research using text mining. The research procedure proceeded to the stages of data collection, data cleaning, text mining, network analysis, and CONCOR analysis. This study conducted a trend analysis of Cosmetology Education Research using text mining, one of the big data analysis methods, to suggest that the beauty field also needs to respond to changes in the times. Through this, it was confirmed that research on the curriculum and learning satisfaction is most actively conducted, and there is a research variations in the school system and the major. However, as this study has been limited to the field of cosmetology education, it is believed that it can be meaningful as more valuable basic data if big data analysis is conducted by expanding the scope of research.

빅데이터 분석 방법을 활용한 교육 방안 연구

This study discusses exploring and materializing educational methods that can be used in actual classes with the analysis and visualization of learning task data. In education, big data analysis technology has been used for analyzing the environment outside class, and this study finds a way to utilize this method in an offline classroom. In particular, it was expected that if learners' assignments were processed through topic modeling, sentiment analysis, and network analysis methods and then visualized and given feedback, it would be helpful for interactive classes.
 In addition, this study focuses on collecting and pre-processing the learner's assignment data so this knowledge can be utilized in a class environment unrelated to computer science. This is based on the spread of big data analysis methods and the development of artificial intelligence technologies such as Chat GPT. Through this, instructors can use computer science technology easily in their class environment.
 This study presents the analysis and visualization of learning task data as a specific example. If this discussion is used, it will be possible for instructors to process and process learning data in a more automated way, even in educational environments where data analysis is not generalized. This is an example of opening the possibility of convergence between computer science and other disciplines.

Biochar application to reduce CO2 emissions from farmland and increase crop yields: a 22-year study based on big data analysis and ideal scenario modeling.

How to reduce the emission of greenhouse gas CO2 from farmland and to improve crop yield is one of the most concerned agricultural ecological environment issues for scientists at present. As an excellent soil conditioner, biochar has a very broad research value and application path in the field. Taking farmland in northern China as the research object, this paper studied the impact of biochar application on soil CO2 emission potential and crop yield in farmland in northern China through big data analysis and modeling methods. The results show that the ideal scenario for increasing crop production and reducing CO2 emissions should be that the raw materials for the preparation of biochar are wheat straw and rice straw; the preparation temperature is 400-500℃, the C/N ratio of biochar is 80-90, the pH of biochar is 8-9, the soil texture is sandy soil or loam soil, soil bulk density is 1.2-1.4gcm-3, the soil pH is less than 6, the soil organic matter content is 10-20gkg-1, and the soil C/N is less than 10; the application amount of biochar is 20-40tha-1; and the use time of biochar is 1year. In view of this, this study selected the data of microbial biomass (X1), soil respiration rate (X2), soil organic matter (X3), soil moisture content (X4), average soil temperature (X5), and CO2 emissions (Y) for correlation analysis and path analysis, and finally obtained the multiple stepwise regression equation between CO2 emissions and various impact factors as follows: Y = - 27.981 + 0.6249 X1 + 0.5143 X2 + 0.4257X3 + 0.3165X4 + 0.2014X5 (R2 = 0.867, P < 0.01, n = 137). Microbial biomass and soil respiration rate directly affect CO2 emissions, reaching a highly significant level (P < 0.01); the second is soil organic matter, soil moisture content, and average soil temperature. The indirect relationship between CO2 emissions and soil average temperature, microbial biomass, and soil respiration rate is the strongest, followed by soil organic matter and soil moisture content.

A machine learning method for the prediction of ship motion trajectories in real operational conditions

This paper presents a big data analytics method for the proactive mitigation of grounding risk. The model encompasses the dynamics of ship motion trajectories while accounting for kinematic uncertainties in real operational conditions. The approach combines K-means and DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) big data clustering methods with Principal Component Analysis (PCA) to group environmental factors. A Multiple-Output Gaussian Process Regression (MOGPR) method is consequently used to predict selected ship motion dynamics. Ship sway is defined as the deviation between a ship and her motion trajectory centreline. Surge accelerations are used to idealise the time-varying manoeuvring of ships in various routes. Operational conditions are simulated by Automatic Identification System (AIS), General Bathymetric Chart of the Oceans (GEBCO), and nowcast hydro-meteorological data records. A Dynamic Time Warping (DTW) method is adopted to identify ship centre-line trajectories along selected paths. The machine learning algorithm is applied for ship motion predictions of Ro-Pax ships operating between two ports in the Gulf of Finland. Ship motion dynamics are visualised along the ship's route using a Gaussian Progress Regression (GPR) flow method. Results indicate that the present methodology may assist with predicting the probabilistic distribution of ship dynamics (speed, sway distance, drift angle, and surge accelerations) and grounding risk along selected ship routes.

음식축제 관광의 활성화를 위한 K-푸드의 홍보방향 : 소셜 빅데이터를 활용한 떡볶이 연관어 분석을 중심으로

In order to present a vision for food tourism necessary for the sustainable growth of K-food and take a global leap, and to revitalize food tourism, we must prepare for the future of food tourism by actively promoting K-food. Therefore, promotion through social media is active in food tourism. Customers share information about the company through social media and communicate with other customers. Because of these characteristics, a significant number of restaurant companies use social media as a marketing channel. The big data analysis methods, which finds information by analyzing vast amounts of Internet-based data, is useful for understanding the meaning and relationship of information. The analysis method was analyzed using the data collection site SomeTrend for ‘text mining’ and ‘semantic network analysis’, and the main keywords were collected as related words of ‘Tteokbokki’, ‘Food Festival’, and ‘K-Food’. Social big data can be used as a way to interpret trends. Based on these research methods, it is a theoretical exploration of ways to connect food festivals and tourism strategies for Tteokbokki, a representative food based on traditional knowledge of K-Food, to meet the needs of food tourism and globalize K-Food. And it is a study to present effective implications from a practical point of view.

Analysis of Factors Influencing the Trust Levels of Kyrgyzstan Residents, Using Neural Network Analysis

Objective: Kyrgyzstan, located in Central Asia, is a country which has a strong will to achieve national development. The aim of this study is to measure the levels of trust of local residents, a highly important factor in national development, and to derive suggestions for improving it. To this end, the primary means employed is to target the residents of Kyrgyzstan and measure the levels of trust they have towards each other. Methods: The study uses data relating to aid projects for rural development that Korea’s Good Neighbors International organization (GNI) is jointly carrying out in Kyrgyzstan along with the Korea International Cooperation Agency (KOICA), a Korean aid provider. In order to carry out the aid project to Kyrgyzstan, these organizations conducted a baseline survey at the initial stage, and the results of this study were used for analysis. As regards the analytical method used in this study, neural network analysis was employed for the questionnaire survey data of 583 people in Kyrgyzstan that was used for the baseline survey. Results: Neural network analysis, a component of the big data analysis method, has recently been in the academic limelight. The analysis revealed that ethnicity had the greatest influence on the trust levels of Kyrgyzstan residents, followed by gender and education level, in that order. Conclusions: From this, it can be seen that multifaceted efforts are needed to increase the levels of trust of peoples other than ethnic Kyrgyzstanis, as they occupy a central position in Kyrgyzstan.

Forecasting the Spread of COVID-19 Using Deep Learning and Big Data Analytics Methods.

To contain the spread of the COVID-19 pandemic, there is a need for cutting-edge approaches that make use of existing technology capabilities. Forecasting its spread in a single or multiple countries ahead of time is a common strategy in most research. There is, however, a need for all-inclusive studies that capitalize on the entire regions on the African continent. This study closes this gap by conducting a wide-ranging investigation and analysis to forecast COVID-19 cases and identify the most critical countries in terms of the COVID-19 pandemic in all five major African regions. The proposed approach leveraged both statistical and deep learning models that included the autoregressive integrated moving average (ARIMA) model with a seasonal perspective, the long-term memory (LSTM), and Prophet models. In this approach, the forecasting problem was considered as a univariate time series problem using confirmed cumulative COVID-19 cases. The model performance was evaluated using seven performance metrics that included the mean-squared error, root mean-square error, mean absolute percentage error, symmetric mean absolute percentage error, peak signal-to-noise ratio, normalized root mean-square error, and the R2 score. The best-performing model was selected and used to make future predictions for the next 61days. In this study, the long short-term memory model performed the best. Mali, Angola, Egypt, Somalia, and Gabon from the Western, Southern, Northern, Eastern, and Central African regions, with an expected increase of 22.77%, 18.97%, 11.83%, 10.72%, and 2.81%, respectively, were the most vulnerable countries with the highest expected increase in the number of cumulative positive cases.

시민력 개념에 관한 연구

The purpose of this paper is to confirm the conceptual features of social civic power. Social civic power is a concept that has recently emerged and is used in civil society, lifelong education, and social welfare. However, since academic discussion and research on the concept have not progressed, there is inclined to define and approach it in various ways depending on the user. In order to achieve the purpose of this papaer, we conducted 1:1 interviews with a total of 15 experts and practitioners related to research, lectures, and activities on social civic power. For analysis, semantic network analysis and CONCOR (CONavergence of iteration CORealtion) analysis were applied among text mining, which is a big data analysis method. As a result, social civic power emphasizes participation in everyday activities in a village or region in various ways, and the participation is based on the purpose of realizing democratic values and community interests. And it focuses on individual or organized civic participation by securing personal capacity. The background of the emergence of social civic power shares the same feature as the new social movement, which focuses on civic activities that value daily closeness and activities rather than checking government power. It is suggested that it is necessary to study the conditions and results of social civil power in the future through this study.