• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.115-122
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• 2014

One of the problems with current youth generations is increasing rate of violence and suicide in their school lives, and this study aims at the design of a sentiment analysis system to prevent suicide by uising big data process. The main issues of the design are economical implementation, easy and fast processing for the users, so, the open source Hadoop system with MapReduce algorithm is used on the HDFS(Hadoop Distributed File System) for the experimentation. This study uses word count method to do the sentiment analysis with informal data on some sns communications concerning a kinds of violent words, in terms of text mining to avoid some expensive and complex statistical analysis methods.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.751-776
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• 2022

Purpose: The purpose of this paper is to present implications by analyzing research trends on CSR, CSV and ESG by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on CSR, CSV and ESG. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "CSR", "CSV" and "ESG" as search terms, and the Korean abstracts and keyword were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, CSR 2,847 papers, CSV 395 papers, ESG 555 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; CSR, CSV, and ESG studies showed that research slowed down somewhat before 2010, but research increased rapidly until recently in 2019. Research have been found to be heavily researched in the fields of social science, art and physical education, and engineering. As a result of the study, there were many keyword of 'corporate', 'social', and 'responsibility', which were similar in the word cloud analysis. Looking at the frequent keyword and word cloud analysis by field and year, overall keyword were derived similar to all keyword by year. However, some differences appeared in each field. Conclusion: Government support and expert support for CSR, CSV and ESG should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to them. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.103-110
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• 2021

The purpose of this study is to check the trend of press articles and research trends in journal papers in the last 10 years, which dealt with the subject of 'university core competencies' with a big data analysis method. The main research methodology of this study applied the BigKinds analysis system and the semantic network analysis methodology. The results are as follows: First, the number of press articles related to university core competencies showed a keyword trend that rapidly increased in December 2014 and the second half of 2020. Related keywords were curriculum, specialization, project group, Ministry of Education, ACE, and competitiveness. Second, the semantic network value between keywords of related research papers showed 554 degree, 18,467 avg. degree, and 0.637 density. The degree of centrality of connection was analyzed in the order of university(1606), competency(1481), core(1349), and core competency(1301). Betweenness centrality was analyzed as core competencies(13.101), university students(13.101), university(13.101), and competencies(13.101). The results of this research are expected to give implications to future research and policy-making, educational program planning and operation, etc. to members of higher education institutions, experts in education policy, and educational scholars.

• The Mathematical Education
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• v.56 no.3
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• pp.319-340
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• 2017

The purpose of this study is to develop teaching and learning materials for the mathematics project inquiry subject. Since this subject is newly opened in the 2015 revised mathematics curriculum, there are no textbooks and materials. Hence it is required to help teachers plan lessons of the mathematics project inquiry subject. For this study, developing directions and objectives are established. Ten hours of lesson plan and teaching and learning materials are also developed for the two themes of 'big data' and 'industrial mathematics'. Suitability and validity of the developed material are verified positively from a survey of 8 teachers and 2 professionals. The detailed result findings are as follows. First, teaching and learning notes are suggested for each lesson plan. They are comprised of building inquiry plan, doing inquiry, summarizing results, and presentation. Second, driving questions of each theme are developed as "What is the big data and where is it used for ?" and "How various is the use of the industrial mathematics ?" respectively. Third, poster-types of each project product are developed. Fourth, three inquiry activity sheets and examples which are theme selection, inquiry plan, and group activity are developed. Fifth, 4 assessment sheets of self, peer, group, and teacher-use are developed.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.463-469
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• 2016

This paper presents a compression-friendly low power test scheme in EDT environment. The proposed approach exploits scan slices reusing to reduce the switching activity during shifting for test scheme based on linear decompressor. To avoid the impact on encoding efficiency from resulting control data, a counter is utilized to generate control signals. Experimental results obtained for some larger ISCAS'89 and ITC'99 benchmark circuits illustrate that the proposed test application scheme can improve significantly the encoding efficiency of linear decompressor.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.717-730
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• 2019

The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.90-99
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• 2023

This paper deals with studying strategy about impact of deep learning (DL) on the factor of Korean economic growth. To study classification of impact factors of Korean economic growth, we suggest dynamic equation of microeconomy and study methods on economic growth impact of deep learning. Next step is to suggest DL model to dynamic equation with Korean economy data with growth related factors to classify what factor is import and dominant factors to build policy and education. DL gives an influence in many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently, young generations will take a big impact on their job selection because generative AI can do well as much as humans can do it everywhere. Therefore, policy and education methods should be rearranged as new paradigm. However, government and officers do not understand well how it is serious in policy and education. This paper provides method of policy and education for AI education including generative AI through analysing many papers and reports, and experience.

• The Journal of Bigdata
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• v.5 no.1
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• pp.11-27
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• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1087-1095
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• 2016

The study aims to analyze factors of box office utilizing big data. The film industry has been increasing in the scale, but the discussion on analysis and prediction of box-office hit has not secured reliability because of failing in including all relevant data. 13 films have sold 10 million tickets until the present in Korea. The study demonstrated laughs and tears as an main interior factors of box-office hit films which showed more than 10 milling tickets power. First, the study collected terms relevant to laugh and tear. Next, it schematizes how frequently laugh and tear factors could be found along the 5-film-stage (exposition - Rising action - crisis - climax - ending) and revealed box-office hit films by genre. The results of the analysis would contribute to the construction of comprehensive database for the box office predictions on future scenarios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1351-1356
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• 2019

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic patients based on the Big Data Platform. As a method of study, second data of 2017 community health survey and Statistics Korea by City·Gun·Gu public office were used and a multi-level analysis was conducted after separating EQ-5D index, individual factor and community factor. As a result, men, age, education level, monthly household income, having economic activity, the number of sports infrastructure were positively associated with the quality of life, and subjective health not good, extremely perceived stress were negatively associated with the quality of life. Research will continue to provide a platform independent of hardware that can utilize the cloud and open source for medical big data analysis in the future.