• Journal of Korea Technology Innovation Society
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• v.16 no.4
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• pp.1163-1186
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• 2013

Big science such as nuclear fusion accelerators shares the characteristic of requiring long-term and massive budget input, human power, and related state-of-the-art technology. Because big science, by nature, thus requires large-scale budgets and facilities yet harbors the possibility of failure, most projects are led by the government. When the actual circumstances are examined, however, such projects are often implemented through the formation of cooperative relations with small and medium businesses (SMBs) possessing outstanding technological capacity. On the other hand, the reality is that the entry of corporations into the business ecosystem of big science is not easy and that even those that have once entered big science likewise fail to find sales outlets for technology that they have developed following the supply of single items, thus leading their technological capacity to lie idle. Consequently, based on an awareness of the problem, the present study seeks to propose strategies for activating the business ecosystem of nuclear fusion and accelerators and to present alternatives regarding which policy tasks must be pursued first by using the analytic hierarchy process (AHP) technique. The present study derived the four policy alternatives of approach, care, expansion, and infrastructures in accordance with the results of empirical analysis to activate the business ecosystem of nuclear fusion and accelerators and analyzed their priority in terms of urgency and effectiveness, the results of which were, in this order: care-approach-expansion-infrastructures. The significance of such research results lie in presenting the policy direction when the government determines which policy task must be pursued first and implements strategies for the activation of the business ecosystem of nuclear fusion and accelerators with limited financial resources in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.211-218
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• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.73-99
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• 2014

Recently, big data have been studied ways to use in various fields. Big data refers to huge amounts of data that could not be addressed by conventional methods. Big data has attracted attention for improving accuracy of decision-making, forecasting in the near future, and creation of new business. In this study, it is an object to develop the utilization plan for big data in the field of technology commercialization. For this reason, we conducted study like case studies, literature review and focus group interview. We have derived the big data utilization plan based on this in the technology commercialization field. It, the data utilization plan, combines with the technology commercialization process of Jolly and it has five sub processes (Imagining, Incubating, Demonstrating, Promoting, Sustaining). In this paper, there is a significance that has emphasized the possibility for big data utilization in the technology commercialization. However, there is a limit to the general interpretation for our study. And we hope to contribute to the expansion of areas of technology commercialization information analysis through this research.

• The Journal of Society for e-Business Studies
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• v.4 no.1
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• pp.59-71
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• 1999

In this paper, we present implemetnations of subcontract management system using intranet. Subcontract management is including the support functions such as process control, official documents exchange, and transmission of subcontract management policy carried out by a big companys subcontract management department. By the use of the developed system, both big company and subcontract company are expected to have benefits from a consistent management of information and a strong access function through the internet. Also, it will reduce time lag ocurred a legacy management between two companies in the past.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.349-352
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• 2015

빅 데이터가 정보통신기술 분야의 핵심 이슈로 부각되면서 관련 기술에 대한 관심이 증가하고 있다. 빅 데이터 분석 도구인 R은 통계 기반의 정보 분석을 가능하게 하는 언어와 환경이다. 본 논문에서는 이를 이용하여 성경데이터를 분석한다. 분석을 통해 신구약, 모세오경, 사복음서별로 어떠한 텍스트가 분포되어 있는지를 빈도 조사를 수행한다.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.95-106
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• 2016

The major thrust of this research focuses on the development of phased big data distribution model based on the big data ecosystem. This model consists of 3 phases. In phase 1, data intermediaries are participated in this model and transaction functions are provided. This system consists of general control systems, registrations, and transaction management systems. In phase 2, trading support systems with data storage, analysis, supply, and customer relation management functions are designed. In phase 3, transaction support systems and linked big data distribution portal systems are developed. Recently, emerging new data distribution models and systems are evolving and substituting for past data management system using new technology and the processes in data science. The proposed model may be referred as criteria for industrial standard establishment for big data distribution and transaction models in the future.

• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.109-118
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• 2016

Big data, thanks to the development of data sciences, has been key words for economic development and governmental policies. This study reviewed how big data has been used with entertainment contents since the uses of big data in the fields have been more popular and the cases making successful business have been reported. To do ends, the changes in production, distribution, and consumption of entertainment contents have been characterized and prime cases have been introduced. Furthermore, Korean production companies of entertainment contents, Bloter and NEOTOUCHPOINT, were selected to investigate how big data has been utilized. It was found that the companies used big data to analyze consumers' behaviors and gain insights of content creation, such as identifying specific elements of enjoyments and conditions to share the contents with others. The domestic entertainment companies are preparing a full-scale of use of big data but, in order to take advantage of big data, collaboration between developers and experts in the field and specific goal-setting and model building are recommended.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.211-213
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• 2013

In these days, the delusions of Big Data and apprehension about them are coming into the picture in many business fields. General techniques for preservation, analysis, and utilization of Big Data are falling short of useful techniques for the volume of fast-increasing data. However, there are some assertions that the power of analysis and prediction of Artificial Intelligence would intensify the power of Big Data analysis. This paper studies on business cases to try to graft the Artificial Intelligence technique onto Big Data analysis. We first research on various techniques of Artificial Intelligence and relations between Artificial Intelligence and Big Data. And then, we perform case studies of Big Data with using Artificial Intelligence and propose some roles of Big Data in the future.

• Knowledge Management Research
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• v.21 no.4
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• pp.243-263
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• 2020

The purpose of this study is to propose the performance measurement model for open big data platform. In order to develop the performance measurement model, we have integrated big data reference architecture(NIST 2018) with performance prism model(Neely et al. 2001) in the platform perspective of open big data. Our proposed model consists of five key building blocks for measuring performance of open data platform as follows: stakeholder contribution, big data governance capabilities, big data service capabilities, big data IT capabilities, and stakeholder satisfaction. In addition, our proposed model have twenty four evaluation indices and seventy five measurement items. We believe that our model could offer both research and practical implications for relevant research.

• Journal of Distribution Science
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• v.13 no.9
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• pp.5-11
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• 2015

Purpose - The purpose of this research is to provide insights that can be used for deliberate decision making around challenging big data investments by measuring the economic value of such big data implementations. Research design, data, and methodology - We perform empirical research through an event study. To this end, we measure actual abnormal returns of companies that are triggered by their investment announcements in big data, or firm size information, during the three-year research period. The research period targets a timeframe after the introduction of big data at Korean firms listed on the Korea stock markets. Results - Our empirical findings discover that on the event day and the day after, the abnormal returns are significantly positive. In addition, our further examination of firm size impacts on the abnormal returns does not show any evidence of an effect. Conclusions - Our research suggests that an event study can be useful as an alternative means to measure the return on investment (ROI) for big data in order to lessen the difficulties or decision making around big data investments.