• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.233-239
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• 2021

Recently, the COVID-19 virus has caused people to stay indoors longer without going out. As a result of this, people's activity decreased sharply, and their weight gained. So people became more interested in health. Home training can be an alternative method to solve this problem. Accordingly, To find out the trends of home training, we collected articles from December 1, 2019, to November 30, 2020, using the news provided by BIG KINDS, a news analysis system. We analyzed frequency analysis, relational analysis according to weighting, and related word analysis with the program using the algorithm developed by BIG KINDS. In conclusion, first, it was found that home training is led by technology and the emergence of artificial intelligence. Second, it can be assumed that people mainly do home training using content and video services related to mobile carriers. Third, people had a high preference for Pilates in the sports category. It can be seen that the number of patent applications increased as the demand for exercise products related to Pilates increased. In the next study, we expect that this study will be used as primary data for various big data studies by supplementing the research methodology and conducting various analyses.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.99-101
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• 2013

본 논문에서는 사춘기 여고생들의 기능성 화장품의 신상품 개발과 성능 향상을 위한 효율적인 정보의 분석과 생산 정책을 위한 SNS 분석시스템을 제안한다. 제안하는 시스템은 여고생들의 기능성 화장품에 관한 SNS 내용을 분석하기 위한 효율적 알고리즘과 방법론을 제안하여 시스템의 처리량을 최대화하고, 각 작업의 수행시간을 최소화한다. 또한 여고생들의 기능성 화장품에 대한 기호 상태를 파악하여, 그 분석 결과를 제품의 개발 및 생산에 반영하기 위한 비주얼 방법론을 함께 제안한다. 따라서 본 논문에서 제안하는 시스템은 단지 화장품에 대한 분석뿐만 아니라 이와 비슷한 소비자의 기호가 빠르게 변화하는 제조업 분야에서 다양하게 응용이 가능하다.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.269-276
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• 2015

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4133-4145
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• 2017

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.739-746
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• 2020

The fourth industrial revolution introduced at world economic forum in 2016 has had huge effects on tourism industries as well as the change of core technologies in ICT such as big data, IoT, etc, This paper proposes the methods to propel tourism of Yoesu city through big data analysis and questionnaires. Sensitive words and positive-negative trend are extracted by Social Metrics and the keywords for Yeosu tour trends are extracted and analyzed by Naver datalab, and the results are visualized by R language. And frequency, difference, factor, covariance and regression analysis in SPSS are executed for the questionnaires for 493 visitors who traveled in Yeosu city. Sentiment analysis for Yeosu tour and maritime cable car shows that positive effect is much more than negative one. The analyses for questionnaires in SPSS show that Yeosu area is statistically significant to tour satisfaction index and tour revitalization for Yeosu, and favorite sightseeing places and searching electronic devices for age groups are different. The sightseeing places such as a maritime park with soft contents that give joyfulness and healing to tourists are highly attracted in both the big data and questionnaires analysis.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.785-790
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• 2019

Convergence and advancement between technologies such as Artificial Intelligence, Big Data, and the Internet of Things have a significant impact on the regional flagship industry. All technical fields are used as a converged technology by connecting between technology and industry. In order to understanding the recent technical trend, it is possible to easily realized the technical trend research and analysis through keyword search using patent information. The purpose of this study is to identify patent trends applied to convergence technology in the 4th Industrial Revolution age in radiation technology development and to present patent trends and analysis for strengthening and utilizing radiation-related industrial technology competitiveness and to apply them to demand technology and forecast future promising technologies.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.25-37
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• 2022

The purpose of this study is to comprehensively check trends in policies, discourses, educational directions and contents, and social issues by deriving the subjective characteristics of peace education and unification education based on big data analysis. The results of this study are as follows. First, 'peace', 'unification', 'education', 'research', 'student', 'school', 'teacher', 'target', and 'Korean Peninsula' were commonly important keywords in peace education and unification education. Second, the top topic of peace education was 'peace education and civic education', and the top topic of unification education was ' sympathy and participation in unification education'. Third, topics that show an upward trend by regime in peace education were 'World Peace and Human Rights' and 'Object and Direction of Peace Education', and 'Subject of Unification Education' as topics that showed an upward trend by regime in unification education. Fourth, in peace education, the centrality of 'peace', 'education', 'student', 'school', and 'peace education' was high, and in unification education, 'unification', 'education', 'unification', 'school', and 'teacher' were high. Based on these results, it was intended to expand the horizon of understanding peace education and unification education, and to provide meaningful implications for establishing policies and conducting follow-up studies.

• Knowledge Management Research
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• v.21 no.3
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• pp.215-228
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• 2020

This study introduces the case of a company that newly jumped into the competitive cosmetics market with a brand concept developed through big data analysis. Skin Reverse Lab, which possesses anti-aging material technology, launched a new brand in the skincare cosmetics market. Using a big data analysis program called Luminoso, SNS data was analyzed in four areas, which were consumer attitudes toward overall cosmetics, skincare products, competitors, and consumers' experiences of product use. The age groups and competitors were analyzed through the emotional analysis technique including context, which is the strength of Luminoso, and insights on consumers were derived through the related word analysis and word cloud techniques. Based on the analysis results, Logically Skin have won various awards in famous magazines and apps, and have been recognized as products that meet global trend standards. Besides, it has entered six countries including the United States and Hong Kong. The Logically Skin case is a case in which a new company entered the market with a new brand by deriving consumer insights only from external data, and it is significant as a case of applying AI-based sentiment analysis.