• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.583-589
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• 2019

One of the core pillars of the Fourth Industrial Revolution is the innovation of the manufacturing industry. From the beginning, the 4th Industrial Revolution appeared in Germany under the concept of 'Industrial 4.0', which means a radical change in the manufacturing industry that can provide super intelligent product production and services based on artificial intelligence and big data. Since manufacturing innovation is a change in the industrial character of the entire nation, the initial role of government is important. Korea also has various policies related to the 4th Industrial Revolution, but there are still various problems to be solved. Therefore, this paper monitors and analyzes the public R&D projects of the advanced countries on manufacturing innovations in the background mentioned above, and through this, the policy implications are drawn.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.7-16
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• 2009

Because the transport policy of Korea has overemphasized road, the physical distribution function of railroad has dwindled a great deal relatively. Recently, the railway has started to be embossed due to the rise of oil prices and environment problems, in addition the government is investing greatly in railroad. The railway corporation took a big step in its history in changing to a public corporation in 2005, and it has been making every possible endeavor to improve management. This research analyzed the trend and stability of the efficiency of railway container handling goods station in korea from 2002 to 2007 based on time of after being changed to a public corporation in 2005 in order to look into the trend of efficiency. The DEA- AR(Data Envelopment Analysis-Assurance Region) and the DEA-Window, widely used as the estimation techniques of the efficiency, were used. According to the results, the efficiency was a little enhanced in 2003 in comparison with 2002, after which it continuously decreased up to 2006 and again rose in 2007. The efficiency of the railway corporation was 0.6777, but after changing to a public corporation, it showed a trend of better efficiency after some transition period had passed.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.39-58
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• 2014

As the importance of big data and related technologies continues to grow in the industry, it has become highlighted to visualize results of processing and analyzing big data. Visualization of data delivers people effectiveness and clarity for understanding the result of analyzing. By the way, visualization has a role as the GUI (Graphical User Interface) that supports communications between people and analysis systems. Usually to make development and maintenance easier, these GUI parts should be loosely coupled from the parts of processing and analyzing data. And also to implement a loosely coupled architecture, it is necessary to adopt design patterns such as MVC (Model-View-Controller) which is designed for minimizing coupling between UI part and data processing part. On the other hand, big data can be classified as structured data and unstructured data. The visualization of structured data is relatively easy to unstructured data. For all that, as it has been spread out that the people utilize and analyze unstructured data, they usually develop the visualization system only for each project to overcome the limitation traditional visualization system for structured data. Furthermore, for text data which covers a huge part of unstructured data, visualization of data is more difficult. It results from the complexity of technology for analyzing text data as like linguistic analysis, text mining, social network analysis, and so on. And also those technologies are not standardized. This situation makes it more difficult to reuse the visualization system of a project to other projects. We assume that the reason is lack of commonality design of visualization system considering to expanse it to other system. In our research, we suggest a common information model for visualizing text data and propose a comprehensive and reusable framework, TexVizu, for visualizing text data. At first, we survey representative researches in text visualization era. And also we identify common elements for text visualization and common patterns among various cases of its. And then we review and analyze elements and patterns with three different viewpoints as structural viewpoint, interactive viewpoint, and semantic viewpoint. And then we design an integrated model of text data which represent elements for visualization. The structural viewpoint is for identifying structural element from various text documents as like title, author, body, and so on. The interactive viewpoint is for identifying the types of relations and interactions between text documents as like post, comment, reply and so on. The semantic viewpoint is for identifying semantic elements which extracted from analyzing text data linguistically and are represented as tags for classifying types of entity as like people, place or location, time, event and so on. After then we extract and choose common requirements for visualizing text data. The requirements are categorized as four types which are structure information, content information, relation information, trend information. Each type of requirements comprised with required visualization techniques, data and goal (what to know). These requirements are common and key requirement for design a framework which keep that a visualization system are loosely coupled from data processing or analyzing system. Finally we designed a common text visualization framework, TexVizu which is reusable and expansible for various visualization projects by collaborating with various Text Data Loader and Analytical Text Data Visualizer via common interfaces as like ITextDataLoader and IATDProvider. And also TexVisu is comprised with Analytical Text Data Model, Analytical Text Data Storage and Analytical Text Data Controller. In this framework, external components are the specifications of required interfaces for collaborating with this framework. As an experiment, we also adopt this framework into two text visualization systems as like a social opinion mining system and an online news analysis system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.548-557
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• 2021

The purpose of this study was to determine the statistical usefulness of using atypical text data collected from media that are easy to collect to overcoming limits of the existing data related to economic activities of disabled people. In addition, by performing semantic network analysis, major issues by period that could not be grasped by statistical analysis were also identified. As a result, semantic network analysis revealed that the initiative of the public sector, such as the central and local government bodies, was strongly shown. On the other hand, in the private purchase sector, it was also possible to confirm the consumption revitalization trend and changes in production activities in the recent issue of Covid-19. While the term "priority purchase" had a statistically significant relation with the other two terms "vocational rehabilitation" and "employment for the disabled". For the regression results, while the term "priority purchase" had a statistically significant association with the other two terms "vocational rehabilitation" and "employment for the disabled". Further, some statistical analyses reveal that keyword data taken from media channels can serve as an alternative indicator. Implications for issue detection in the field of welfare economy for the disabled is also discussed.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• Journal of Fashion Business
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• v.27 no.3
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• pp.132-149
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• 2023

This study aims to analyze Instagram hashtags based on big data to investigate changes in consumer trends and perceptions of vegan fashion, and to derive strategies for revitalizing vegan fashion brands based on derived results. Among social media, Instagram was selected as a collection channel, and Instagram hashtags for 'Vegan Fashion' were collected from July 1, 2021 to December 31, 2021. After conducting semantic network analysis with the Ucinet 6 program based on the collected data, the CONCOR analysis on vegan fashion showed the following four clusters: 'Veganism practiced with fashion', 'Bag type of vegan fashion brand', 'Sharing vegan fashion', and 'Diversification of eco-friendly products'. Analysis results showed that the Instagram hashtag for vegan fashion confirmed the MZ generation's increased interest in vegan fashion and their thoughts to recommend and share frequently used items or brand products to people around them. CONCOR analysis of vegan fashion brands showed the following four groups: 'Differentiating the material of vegan bags', 'Eco-friendly products of vegan fashion brands', 'Interest in vegan shoes', and 'Donation campaign of vegan fashion brands'. CONCOR analysis on Meaningout showed the following four clusters: 'MZ Generation's Meaningout Start-up', 'Recommendation Platform for Skin Products', 'Value Consumption Trend for Eco-friendly Clothing', and 'Interest in Eco-friendly Packaging'. The results of this study on vegan fashion, a practical eco-friendly movement that can require changes in social responsibility and perception as issues that directly affect animals, the environment, and humans, are expected to provide basic data to help domestic vegan fashion brands develop marketing strategies.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.73-86
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• 2023

The purpose of this study is to prepare basic data that can be applied to the development of personalized programs in which chronic patients can actively participate in health care on their own, by analyzing the relationship between health literacy, level of metal health, and level of life health of patients with chronic diseases. For the study, the Korean Medical Panel's annual data(Version 2.1) was used, and 4,095 people aged 19 or older with chronic diseases and without disabilities were extracted, and frequency analysis, t-test, ANOVA, and chi-squared goodness of fit test, etc. were performed with IBM SPSS Statistics 26.0. As a result, it was found that the higher health literacy, the higher level of mental health and level of life health. In addition, the distribution between health literacy, level of mental health, and level of life health was found to be different from each other. Respondents with higher ability to health literacy tend to evaluate level of metal health and life health lower, and the rate of change in this trend was relatively higher than the rate of change in the tendency to evaluate level of mental health and life health higher in respondents with lower ability to health literacy.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.12-28
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• 2024

This study aims to identify the trend of changes in tourist floating population before and after COVID-19 in major tourist destinations in Jeju Island through spatial analysis. Seongsan-eup and Andeok-myeon in Jeju Island were selected as the research area, and the research period was set at 1 year before and 2 years after the COVID-19 outbreak. For the analysis, mobile floating population data was refined and processed to calculate floating population distribution and floating population increase/decrease data. This was converted into spatial data and an overlay analysis was performed with location data of major tourist attractions. As a result of the analysis, it was confirmed that the floating population of indoor tourist attractions and small facilities decreased immediately after COVID-19, and that in open coastal areas or large facilities, the floating population decreased less or actually increased. In conclusion, in tourism development, it is necessary to identify changes in floating population according to the characteristics of tourist facilities, and it is necessary to develop tourism facilities and strategies that can respond to risk situations such as pandemics when developing tourist destinations.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.71-101
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• 2017

Since late 2016, protectionism has been a major trend in world trade with the Great Britain exiting the European Union and the United States electing Donald Trump as the 45th president. Consequently, there has been a huge public outcry regarding the negative prospects of heavy industry firms in Korea, which are highly dependent upon international trade with Western countries including the United States. In light of such trend and concerns, we have tried to predict business performance of heavy industry firms in Korea with data regarding trade policy of the United States. United States International Trade Commission (USITC) levies countervailing duties and anti-dumping duties to firms that violate its fair-trade regulations. In this study, we have performed data analysis with past records of countervailing duties and anti-dumping duties. With results from clustering analysis, it could be concluded that trade policy trends of the Unites States significantly affects the business performance of heavy industry firms in Korea. Furthermore, we have attempted to quantify such effects by employing long short-term memory (LSTM), a popular neural networks model that is well-suited to deal with sequential data. Our major contribution is that we have succeeded in empirically validating the intuitive argument and also predicting the future trend with rigorous data mining techniques. With some improvements, our results are expected to be highly relevant to designing regulations regarding heavy industry in Korea.