• Journal of Digital Convergence
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• v.20 no.5
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• pp.463-476
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• 2022

After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.253-263
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• 2016

We analyzed the social issue 'image of the elderly' using Big Data and Social Network Analysis. First, we analyzed the words extracted by the text mining technique by inputting the keyword 'elderly'. As a result of analysis, the image of the elderly viewed through media such as cafes, blogs, etc. Representing the trend of the public was using the word 'Senior' the most. The image of the elderly is expressed using the word having the highest frequency in the top 10, "The elderly are 'Senior' people who are respected by society, they are organized to earn money, to earn their qualifications, to health, and to 'Seniors' who desire to work healthy up to 100 years old". The purpose of this study is to differentiate from the existing analysis method by analyzing the macro-level image of the elderly including the social discourse by collecting vast amount of data and analyzing it with the social networking technique. When the image of the elderly that the public perceives is positively expressed as 'Senior', it can be said that the direction of the current elderly policy is evaluated as a desirable direction. On the other hand, it was able to feel the 'desire' of the public who wanted to be evaluated. Therefore, the policy direction of the elderly to be applied in the future should be the policy that enables the elderly to be perceived as 'Necessary existence' in society by taking on social roles. In addition, we proposed to implement the policy of the elderly that reflects priorities such as job creation, welfare, and alienation that can activity and maintain health.

• Journal of the Korean Society of Earth Science Education
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• v.17 no.2
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• pp.153-167
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• 2024

This study explored the errors and context occurred during high school students' data analysis processes. For the study, 222 data inquiry reports produced by 74 students from 'A' High School were collected and explored the detailed error types in the data analysis processes such as data collection and preprocessing, data representation, and data interpretation. The results of study found that in the data interpretation process, students had a somewhat insufficient understanding of seasonal variations and periodic patterns about weather elements. And, various types of errors were identified in the data representation process, such as basic unit in graphs, legend settings, trend lines. The causes of these errors are the feature of authoring tools, misconceptions related to weather elements, and cognitive biases, etc. Based on the study's results, educational implications for big data education, a significant topic in future science education, were derived. And related follow-up studies were suggested.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.275-282
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• 2020

Social interest on high-quality specialty coffee is increased due to customers' growing experience upon coffee and recent change of coffee culture, which is taking one step further from putting emphasis on not just price and quality but also psychological satisfaction. As a culture of drinking coffee and giving much value on its taste and flavor, a number of customers increasingly demand coffee which is probable to suit one's taste. Likewise, the number of specialty coffee shops is increasing with growing qualities of their coffee. Therefore, the purpose of this study is to analyze the main attributes of specialty coffee and to build a marketing system for specialty coffee shops. The text mining on domestic web portal sites by online big-data analysis is used to extract components of properties of specialty coffee and analyze the degree of how the elements affect the properties. According to the result of the study, words related to coffee taste, coffee beans and baristas were found to play a central role in the properties of specialty coffee.

• Journal of Korean society of Dental Hygiene
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• v.20 no.5
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• pp.733-741
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• 2020

Objectives: The purpose of the study was to analyze papers published in the Journal of Korean Society of Dental Hygiene (JKSDH) and to identify the current state of dental hygiene research and recommend directions for future research. Methods: A total of 315 articles published between 2016 to 2018 were reviewed using analysis criteria. Results: The number of grant research and experimental studies during 2016-2018 was higher than that before 2015. Quantitative studies were dominant and oral health was the most common research topics. The number of published papers, the proportion of reported reliability of instrument studies, reported ethical consideration and studies that described criteria for sample size had increased. The most common sampling of quantitative studies were convenient sampling and questionnaire and big data of data collection methods were the most. Conclusions: Findings of this study indicate that the recent trends in dental hygiene research and the direction of dental hygiene research and will improve the quality of papers and promote the reputation of JKSDH as an international journal.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.627-635
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• 2022

As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.492-501
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• 2019

To use the system safely in cyberspace, you need to use a security solution that is appropriate for your situation. In order to strengthen cyber security, it is necessary to accurately understand the flow of security from past to present and to prepare for various future threats. In this study, information security words of security/hacking news of Naver News which is reliable by using text mining were collected and analyzed. First, we checked the number of security news articles for the past seven years and analyzed the trends. Second, after confirming the security/hacking word rankings, we identified major concerns each year. Third, we analyzed the word of each security solution to see which security group is interested. Fourth, after separating the title and the body of the security news, security related words were extracted and analyzed. The fifth confirms trends and trends by detailed security solutions. Lastly, annual revenue and security word frequencies were analyzed. Through this big data news analysis, we will conduct an overall awareness survey on security solutions and analyze many unstructured data to analyze current market trends and provide information that can predict the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.289-294
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• 2014

In the recent enterprises and are utilizing the CRM using data mining techniques and new marketing plan. However, data mining techniques are necessary expertise, general public access is difficult, it will be subject to constraints of time and space. in this paper, in order to solve this problem, we have proposed a Mobile CRM applying the data mining method. Thus, to analyze the structure of an existing CRM system, and defines the data flow and format. Also, define the process of the system, was designed sales trend analysis algorithm and customer sales recommendation algorithm using data mining techniques. Evaluation of the proposed system, through the test scenario to ensure proper operation, it was carried out the comparison and verification with the existing system. Results of the test, the value of existing programs and data matches to verify the reliability and use queries the proposed statistical tables to reduce the analysis time of data, it was verified rapidity.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.2
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• pp.111-122
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• 2023

This study aims to collect Big Data related to DIY clothing, analyze the results on a year-by-year basis, understand consumers' perceptions, the status, and reality of DIY clothing. The reference period for the evaluation of DIY clothing trends was set from 2012 to 2022. The data in this study was collected and analyzed using Textom, a Big Data solution program certified as a Good Software by the Telecommunications Technology Association (TTA). For the analysis of fabric-related DIY products, the keyword was set to "DIY clothing", and for data cleansing following collection, the "Espresso K" module was employed. Also, via data collection on a year-by-year basis, a total of 11 lists were generated and the collected data was analyzed by period. The following are the findings of this study's data collection on DIY clothing. The total number of keywords collected over a period of ten years on search engines "Naver" and "Google" between January 1, 2012 and December 31, 2022 was 16,315, and data trends by period indicate a continuous upward trend. In addition, a keyword analysis was conducted to analyze TF-IDF (Term Frequency-Inverse Document Frequency), a statistical measure that reflects the importance of a word within data, and the relationship with N-gram, an analysis of the correlation concerning the relationship between words. Using these results, it was possible to evaluate the popularity and growing tendency of DIY clothing products in conjunction with the evolving social environment, as well as the desire to explore DIY trends among consumers. Therefore, this study is valuable in that it provides preliminary data for DIY clothing research by analyzing the status and reality of DIY products, and furthermore, contributes to the development and production of DIY clothing.