• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.567-581
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• 2017

The purpose of this study is to analyze the facilitating and conflicting factors on the emerging technologies and trends predicted to impact future education in Korea. To do this, open online questionnaires on 20 emerging technologies and trends derived from a comprehensive literature review were completed by 24 experts in research, policy, schools, and corporate fields, and a content analysis of the collected qualitative data was conducted. As a result of the study, the effectiveness of the content and the maturity of technology were found to be the most important facilitating factors and obstacles. In addition, the potential for innovative teaching and learning methods and motivation, and the maturity and popularity of technology were found to be the main facilitating factors. On the other hand, health problems and negative effects on students in ethical aspects, the lack of research and development, and poor networks and infrastructures in terms of education environment were found to be the main impeding factors of emerging technologies and trends.

• Electronics and Telecommunications Trends
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• v.35 no.3
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• pp.45-54
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• 2020

The cloud computing industry has focused on establishing a cloud-based business environment for enterprises with efforts to convert using their own on-premise computing infrastructures to using cloud services. With these efforts, using cloud services has become natural, especially for the IT industry. The cloud computing industry is moving toward proliferation of the cloud computing environment into various evolving industries. Along with industrial trends, new technical trends such as edge computing and multi-cloud are emerging. These trends are expected to create new business models and develop related service ecosystems, providing new opportunities for service providers and new experiences for users. A mong those emerging technologies, multi-cloud technology is expected to realize unlimited global cloud computing resources by unifying cloud resources from multiple public cloud service providers. In this paper, we introduce the concept and related trends of multi-cloud technology. Subsequently, we analyze the main functionalities and several use cases of multi-cloud technology. Finally, we summarize the effects and usefulness of multi-cloud technology in the domestic cloud industry.

• The Journal of Information Systems
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• v.17 no.3
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• pp.235-254
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• 2008

This article describes the latest development of a generic approach to detecting emerging trends and transient in scientific literature. The work makes substantial theoretical and methodological contributions to progressive Information analysis. A specialty is conceptualized as a time variant duality research front concepts in information science. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The contributions of the approach is that the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms. The modeling process is implemented in RADERS, and applied to the analysis of telecommunication field. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1171-1186
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• 2011

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

• Broadcasting and Media Magazine
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• v.6 no.1
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• pp.88-96
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• 2001

• Journal of Distribution Science
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• v.12 no.10
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• pp.37-45
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• 2014

Purpose - This study aims to analyze the relationship between trends in innovative EU industries and market distribution in smaller emerging markets under an open economy. Research design, data, and methodology - Although innovation was well-distributed, due to socio-economic factors following European integration, CEE had not achieved sustainable economic growth. However, this paper analyzes the differences among changes in CEE innovation for smaller emerging markets dominated since 2000. Market distribution has facilitated new markets for innovative industries, according to EU surveys and economic indicators. Results - The dominance of the local industrial market distribution has deterred innovation investment the survey shows that innovation investment has been shrinking, despite the EU's open innovation policy for CEE employment and R&D. For the CEE case, there were expectation gaps and uncertainty about whether to use the new distribution dominance or TNCs' innovation in smaller emerging countries without local industrial innovation. Conclusions - Innovation generates market growth and distribution power however, small growth requires stimulation, and creativity and innovative edge need further focus in local public and corporate strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.1022-1034
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• 2023

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-associated research abstracts collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.71-96
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• 2023

This study aimed to investigate key research areas in the field of Library and Information Science (LIS) by analyzing trends and identifying emerging topics. To facilitate the research, a collection of 40,897 author keywords from 11,252 papers published in the past 30 years (1993-2022) in five journals was gathered. In addition, keyword analysis, as well as Principal Component Analysis (PCA) and correlation analysis were conducted, utilizing variables such as the number of articles, number of authors, ratio of co-authored papers, and cited counts. The findings of the study suggest that two topics are likely to develop as promising research areas in LIS in the future: machine learning/algorithm and research impact. Furthermore, it is anticipated that future research will focus on topics such as social media and big data, natural language processing, research trends, and research assessment, as they are expected to emerge as prominent areas of study.

• 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.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.221-226
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• 2024

This study used text mining, a big data analysis technique, to explore XR trends in South Korea. For this research, I utilized a big data platform called BigKinds. I collected data focusing on the keyword 'XR', spanning approximately 14 years from 2010 to 2024. The gathered data underwent a cleansing process and was analyzed in three ways: keyword trend analysis, relational analysis, and word cloud. The analysis identified the emergence and most active discussion periods of XR, with XR devices and manufacturers emerging as key keywords.