• Journal of Korea Game Society
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• v.19 no.4
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• pp.27-36
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• 2019

Approaches to find hidden values using various and enormous amount of data are on the rise. As big data processing becomes easier, companies directly collects data generated from users and analyzes as necessary to produce insights. User-based data are utilized to predict patterns of gameplay, in-game symptom, eventually enhancing gaming. Accordingly, in this study, we tried to analyze the gaming strategy and user activity patterns utilizing Battlegrounds in-game data to detect the in-game hack.

• Journal of Fashion Business
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• v.26 no.3
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• pp.138-154
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• 2022

The purpose of this study is to identify the characteristics of domestic American casual fashion trends using big data text mining analysis. 108,524 posts and 2,038,999 extracted keywords from Naver and Daum related to American casual fashion in the past 5 years were collected and refined by the Textom program, and frequency analysis, word cloud, N-gram, centrality analysis, and CONCOR analysis were performed. The frequency analysis, 'vintage', 'style', 'daily look', 'coordination', 'workwear', 'men's wear' appeared as the main keywords. The main nationality of the representative brands was Japanese, followed by American, Korean, and others. As a result of the CONCOR analysis, four clusters were derived: "general American casual trend", "vintage taste", "direct sales mania", and "American styling". This study results showed that Japanese American casual clothes are influenced by American casual clothes, and American casual fashion in Korea, which has been reinterpreted, is completed with various coordination and creative styles such as workwear, street, military, classic, etc., focusing on items and brands. Looks were worn and shared on social networks, and the existence of an active consumer group and market potential to obtain genuine products, ranging from second-hand transactions for limited edition vintages to individual transactions were also confirmed. The significance of this study is that it presented the characteristics of American casual fashion trends academically based on online text data that the public actually uses because it has been spread by the public.

• Journal of Fashion Business
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• v.27 no.1
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• pp.1-15
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• 2023

This study divided articles into two time periods, from 2012 to 2022, with the aim of using big data analysis to look at patterns in the ecosystem of fashion start-ups. The research method extracted top keywords based on TF(Term Frequency) and TF-IDF(Term Frequency-Inverse Document Frequency), analyzed the network, and derived centrality values. As a result of comparing the first and second fashion startup ecosystems, elements of policy, support, market, finance, and human capital were derived in the first period. In addition, in the second period, elements of policy, support, market, finance, and culture were derived. In the first period, the fashion startup ecosystem focused on fostering new designer startups by emphasizing support, finance, and human capital factors and focusing on policies. Meanwhile, in the second period, online-based fashion platform startups and fashion tech startups appeared with the support of digital transformation and fulfillment services triggered by COVID-19(Corona Virus Disease 19), private finances were emphasized, and cultural factors were derived along with success stories of fashion startups. This study is meaningful in that it helps in developing strategies for fashion startups to grow into sustainable companies.

• Journal of Fashion Business
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• v.27 no.4
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• pp.50-66
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• 2023

The purpose of this study was to examine the word distribution and topic distribution of slow fashion appearing on YouTube in detail and identify the characteristics and aspects related to fashion design through big data analysis and content analysis methods. The specific research results were as follows. First, in the results of the word distribution analysis, "item" appeared the most, 203 times. Also, "one-piece" was a point to pay attention to, as the item had the highest frequency. Second, a total of 5 topics were defined in the topic distribution analysis: topic 1 was "vintage products," topic 2 was "fashion items," topic 3 was "eco-friendly," topic 4 was "life quality emphasis," and topic 5 was "prudent consumption." Third, looking at the relationship between word distribution and topic distribution above, Korean slow fashion on YouTube was actively selecting related design elements that express vintage images in clothing life regardless of trends. In addition, there was a tendency to pursue various basic and high-quality items. Other than those findings, basic items tended to be reinterpreted in various ways through styling methods matched to the vintage image. Lastly, the tendency of slow and small-volume production appeared to emphasize handicrafts and the cultural values of fashion products.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

• Korea Trade Review
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• v.47 no.4
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• pp.137-159
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• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.305-311
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• 2014

The manufacturing industry early have been introducing automation and information systems of the engineering and production process for getting competitive advantage. one of the typical information systems is MES(Manufacturing Execution System) and it keeps evolving. As Big Data showed up nowadays, application method of Big Data to MES is also being sought. First, this study will do preceding research and cases study on the application of Big Data in the manufacturing industry. Then, it will suggest application Plan of Big Data to MES.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.41-50
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• 2020

The paper aims to investigate auditors, auditing firms and other external factors that affect quality of information technology audit in Vietnam. We conducted 2 types of data collections including direct and on survey. For direct survey, we sent directly to auditors at the training classes organized by State Securities Exchanges Commission. An online survey was established and Google doc link was provided to the Big4 and non-Big4 auditors. We received 138 survey responses in that 90 auditors came from Big4 and 48 auditors from non-Big4 firms. The data are analyzed using a factor analysis and compare means approaches to illustrate the potential IT audit quality factors and identify differences between two groups of auditors. The results show that independence and accounting knowledge and audit skills are the most important factors. And since external auditors perform many assurance services, the independence is critical. The result also shows that the auditors need to have enough competent and professional skills when conducting an audit, especially within an IT environment that requires high quality. The findings suggest a similar pattern of two groups in the context of Vietnam and some factors of auditors and auditing firms appear to have a statistically significant impact on quality of IT audit.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.13-18
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• 2022

The role of artificial medical intelligence through medical big data has been focused on data-based medical device business and medical service technology development in the field of diagnostic examination of the patient's current condition, clinical decision support, and patient monitoring and management. Recently, with the 4th Industrial Revolution, the medical field changed the medical treatment paradigm from the method of treatment based on the knowledge and experience of doctors in the past to the form of receiving the help of high-precision medical intelligence based on medical data. In addition, due to the spread of non-face-to-face treatment due to the COVID-19 pandemic, it is expected that the era of telemedicine, in which patients will be treated by doctors at home rather than hospitals, will soon come. It can be said that artificial medical intelligence plays a big role at the center of this paradigm shift in prevention-centered treatment rather than treatment. Based on big data, this paper analyzes the current status of artificial intelligence technology for chronic disease patients, market trends, and domestic and foreign company trends to predict the expected effect and future development direction of artificial intelligence technology for chronic disease patients. In addition, it is intended to present the necessity of developing digital therapeutics that can provide various medical services to chronically ill patients and serve as medical support to clinicians.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.139-151
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• 2023

This study aims to gain insights into ChatGPT, which has recently received significant attention. The study utilized a mixed method involving case studies and news big data analysis. ChatGPT can be described as an optimized language model for dialogue. The question arises whether ChatGPT will replace Google search services, posing a potential threat to Google. It could hurt Google's advertising business, which is the foundation of its profits. With AI-based chatbots like ChatGPT likely to disrupt the web search industry, Google is establishing a new AI strategy. The study used the BIG KINDS service and analyzed 2,136 articles over six months, from August 23, 2022, to February 22, 2023. Thirty of these articles were written in 2022, while 2,106 have been reported recently as of February 22, 2023. Also, the study examined the contents of ChatGPT by utilizing literature research, news big data analysis, and use cases. Despite limitations such as the potential for false information, analyzing news big data and use cases suggests that ChatGPT is worth using.