• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.287-294
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• 2022

The purpose of this study is to find out consumers' perceptions of "metaverse," a newly spotlighted technology, through big data analysis as a non-face-to-face society continues after the outbreak of COVID-19. This study conducted a big data analysis using text mining to analyze consumers' perceptions of metaverse before and after COVID-19. The top 30 keywords were extracted through word purification, and visualization was performed through network analysis and concor analysis between each keyword based on this. As a result of the analysis, it was confirmed that the non-face-to-face society continued and metaverse emerged as a trend. Previously, metaverse was focused on textual data such as SNS as a part of life logging, but after that, it began to pay attention to virtual reality space, creating many platforms and expanding industries. The limitation of this study is that since data was collected through the search frequency of portal sites, anonymity was guaranteed, so demographic characteristics were not reflected when data was collected.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.1-10
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• 2021

This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.

• Fashion & Textile Research Journal
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• v.25 no.5
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• pp.549-559
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• 2023

Along with the transition to the fourth industrial revolution, the possibility of metaverse-based innovation in the fashion field has been confirmed, and various applications are being sought. Therefore, this study performs meaning structure analysis and discusses the prospects of meta fashion using big data. From 2020 to 2022, data including the keyword "metaverse + fashion design" were collected from portal sites (Naver, Daum, and Google), and the results of keyword frequency, N-gram, and TF-IDF analyses were derived using text mining. Furthermore, network visualization and CONCOR analysis were performed using Ucinet 6 to understand the interconnected structure between keywords and their essential meanings. The results were as follows: The main keywords appeared in the following order: fashion, metaverse, design, 3D, platform, apparel, and virtual. In the N-gram analysis, the density between fashion and metaverse words was high, and in the TF-IDF analysis results, the importance of content- and technology-related words such as 3D, apparel, platform, NFT, education, AI, avatar, MCM, and meta-fashion was confirmed. Through network visualization and CONCOR analysis using Ucinet 6, three cluster results were derived from the top emerging words: "metaverse fashion design and industry," "metaverse fashion design and education," and "metaverse fashion design platform." CONCOR analysis was also used to derive differentiated analysis results for middle and lower words. The results of this study provide useful information to strengthen competitiveness in the field of metaverse fashion design.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.157-167
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• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.141-156
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• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.381-388
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• 2023

This thesis is purposed to develop a regional-customized education program selection model using big data analysis. Based on the literature review, the concepts and characteristics of big data and lifelong education are analyzed. In addition, this thesis presents how to collect the data for lifelong education and to use big data suitable for the characteristics of lifelong education. Based on these results, a regional- customized lifelong education program selection model is developed. The regional customized lifelong education program model is developed by the following six steps. The customized education program model proposed in this study has a high degree of flexibility in terms of practical use, as it can be utilized in real-time data provision methods such as the nationally approved Lifelong Learning Personal Status Survey without the need for analysis one year later, allowing for selective analysis and future predictions. It is clear that there is a significant need and value for big data in the education field. Furthermore, all programs used in the sample model are provided free of charge, and due to the programming nature, the community is actively engaged in exchanges, making it very easy to modify and improve for the development of a more complete education program model in the future.

• Journal of the Korean Society of Food Culture
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• v.19 no.3
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• pp.336-347
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• 2004

This study was conducted to investigate eating-out behavior patterns of youths, especially junior high and senior high school students. 1600 questionnaire surveys were distributed and 1487 were used for analysis. In order to consider regional differences as well as overall characteristics of youths' eating-out behaviors, the subjects were evenly sampled from north Seoul, south Seoul, big cities, middle/small cities and small towns. As for the frequency of eating-out, 62.7% of respondents answered once to twice per week. For the can of more than 5 times of eating-out per week, the respondents from south Seoul showed the highest frequency. For the case of no eating-out, the highest frequency was shown from the small towns. As for the most frequently visited place for eating-out, 33.6% of respondents answered Korean style restaurants, and 17.6% Boon-sik(Sanck-bar). Regarding the preference of Korean style restaurants, the highest rate was shown from the residents of big cities. For the question of when they eat out, 89.6% answered dinner and 6.3% lunch. For the question about reason of choosing particular restaurants, 61.5% of respondents referred to tastes and 16.6% price. For the question of the most important reason of eating out, 52.6% point out 'meal solution' and 25.6% 'for meeting.' As for the people accompanied when eating out, 67.2% of the respondents answered family. For the cost of eating out per person, 45.7% of the respondents spent 2000-4000 won for lunch; 31.1% spent 5000-10,000 won for dinner; 33.7% of the respondents spent more than 20,000 won for the special events. Regarding the regional differences of eating-out cost, respondents from south Seoul tended to spend the biggest amount of money for lunch, dinner and special day.

• The Journal of the Korean dental association
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• v.5 no.1
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• pp.60-62
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• 1964

The Author have had a case of Salivary-stone in the posterior of Warton's duct in the right Side . 1. The patient was 24 years-old R.O.K.a Soldexr. 2. The salivary stone was 1.6cm by 2.11 cm in big size. 3. There was a History of pain at meal-time, and swelling of mandible of Right region.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.30 no.6
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• pp.40-46
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• 2012

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1047-1059
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• 2015

This study will predict risk factors associated with cigarettes in Korea by analyzing the social big data collected from the internet such as blogs, cafes, and SNSes in Korea, using data mining techniques. The key analysis results are as follows. First, when "raising cigarette price"is mentioned online, the negative group (i.e., the proportion of people holding negative views about smoking) increased from 58.6% to 74.8%, and when "lung cancer" is mentioned, it increased to 73.1%. Second, with regard to cigarettes in general, the positive group (i.e., the proportion of people holding positive views about smoking) decreased by 5.6% after the raising of cigarette prices, while the negative group increased by 6.1%. Third, when policies related to "FCTC, raising cigarette price, non-smoking laws, smoking regulations, non-smoking ads, and nonsmoking business" are more frequently mentioned online, the positive group tended to decrease. Finally, when "non-smoking drugs, non-smoking patches, and non-smoking gums" are more frequently mentioned online, the positive group tended to decrease. However, when "electronic cigarettes and supplements" are more frequently mentioned online, the positive group increased.