• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.442-445
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• 2001

본 연구는 현재 정부의 규제완화 및 다양한 대체서비스의 등장으로 인해 점차 시장내 경쟁이 심화되어가고 있는 기업용데이터서비스 시장에 있어 향후 경쟁상황이 어떻게 진행될지에 대한 연구를 통하여 이에 속한 기업들의 향후 시장전략을 제시하는 것을 목적으로 하고 있다. 현재 기업용데이터서비스 시장의 경우 가장 큰 특징이 바로 신규 대체서비스인 ATM 서비스의 등장이고, 이들 서비스에 의해 전체 시장규모가 얼마나 성장할 것인지, 전체 시장에서 개별 서비스들의 기여도는 어느 정도가 되는지가 최대 관심사이다. 이에 대한 문제를 해결하기 위해 본 연구에서는 신규 대체 서비스가 시장에 진입하였을 때 파급정도가 얼마나 되는지를 추정하여 수요 예측과 시장 상황 분석을 하였다. 이에 본 연구결과를 통해 사업자들은 향후 시장경쟁 상황을 예측할 수 있기 때문에 이에 대해 적절한 사업전략을 수립하는데 큰 도움을 얻게 되리라 기대한다.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.723-732
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• 2022

The success of enterprise information system(EIS) is crucial to align with corporate strategies and eventually attain corporate goals. Since one of the factors to information system success is system use, managerial efforts to measure the level of EIS utilization is vital. In this paper, the EIS utilization level is analyzed using system access log data. In particular, process sequence patterns and clustering of similar functions are identified in more detail based on a process mining method, in addition to basic access log statistics. The result of this research can be used to improve existing information system design by finding real IS usage sequences and function clusters.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.347-348
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• 2023

본 논문에서는 실제 규모 데이터센터의 3D 모델링을 기반으로 대상 공간별 화재 시나리오를 선정하여 화재 및 피난 시뮬레이션을 수행하였다. FDS와 Pathfinder는 full coupling 방식을 사용할 수 없는 한계가 있으며 semi coupling의 경우 가시화에는 도움이 되나 결과에 영향을 주지 않는다. 따라서 재실자의 피난 상황 시 경로에 대한 안전성과 화재 위험 노출 정도를 시각적으로 분석하는 것이 가능한 semi coupling과 시뮬레이션 결과 데이터 분석을 병행하여 수행하였다. 전산실의 경우 서버의 기능상실 한계 온도가 32도이기 때문에 서버 기능 정지 상황에 도달하는 시간을 중점적으로 분석하였다. 전산실은 업무 및 고객 서비스와 관련된 모든 데이터들을 저장하기 위해 항시 기동 되어야 하는데 전산실 내 화재가 발생할 경우 1~2분 이내 서버 기능이 정지되는 상황이 발생하였다. 따라서, 서버가 안전하게 계속 동작하기 위해서는 전력 계측 및 제어 케이블 열화, 서버 장치의 건전성이 유지되어야 하며 초기 화재를 빠르게 감지하여 진압하여야 한다. 피난 시뮬레이션의 경우 가시도를 상실하게 되는 시간이 약 195초(5m 미만) 인근으로 인원이 해당 층을 완전히 벗어나는 데 걸리는 시간이 약 125.6초였던 것을 보면 대피하기에 충분한 허용 피난시간(ASET)을 확보하고 있음을 알 수 있었다.

• The Journal of Bigdata
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• v.6 no.1
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• pp.13-22
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• 2021

The purpose of this study is to develop and demonstrate a framework for evaluating the maturity of big data for effective data strategy establishment and efficient investment of companies. By supplementing the shortcomings of the evaluation developed so far, a framework was developed to evaluate the maturity of a company's big data in an integrated process. As a result, four evaluation areas of 'Vision and Strategy', 'Management', 'Analysis' and 'Utilization', assessment items for each area, detailed content, and criteria for each stage were derived. This was verified through a survey of entrepreneurs, and the maturity level of big data of domestic companies was confirmed. As a future research direction, it is proposed to develop detailed assessment factors according to the characteristics of each industry, to develop a data utilization framework according to the assessment results, and to improve validity and reliability through adjustment of verification targets.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.236-238
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• 2015

최근 다양한 데이터를 수집 및 분석하여 급변하는 기업에서 고객 니즈의 변화에 능동적인 대용이 필요하나 중소기업의 경우 비용 및 인력 문제로 인해 데이터 분석 및 정보를 수집하는데 어려움이 있다. 따라서 본 논문에서는 중소기업에서 다양한 고객 니즈 분석을 통해 맞춤형 상품과 서비스 제공이 가능하고, 중소기업들의 전략적이고 신속한 의사결정 지원을 위해서 BI, 프라이빗 클라우드, R-엔진을 분석하여, 프라이빗 클라우드 + R 엔진 기반의 BI 서비스 어플라이언스 설계 하였다.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.241-248
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• 2014

It has been expanded the bandwidth of data usages due to the rapid developments of information technology and infra hardware and then it was proposed to new paradigm of Big Data era. It has a trend to increase a Big Data technology and its performance gradually, thus enterprises have realized the importance of Data and the movement to take advantage of Big Data becomes active. This study has been performed to verify the importance through select the factors in order to active adoption of Big Data technology and utilization when enterprises use Big Data. It was selected that Big Data characteristic factors are the natures of predictability, manageability, affordability, competitiveness, creativity, responsiveness and supportability on the study. It is verified and showed that manageability were influenced to introduce Big Data in order, at the result of survey and statistics for enterprise practitioners who have big data experience.

• Information and Communications Magazine
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• v.29 no.11
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• pp.55-66
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• 2012

빅 데이터의 관심이 인프라 및 분석기술 자체에서 가치창조 측면으로 이동하면서 가치정보를 효율적으로 발굴, 분석, 추출, 활용하기 위한 차세대 고급 분석 기술 및 기법이 요구되고 있다. 이에 빅 데이터 활용기반구축을 위한 정부 및 기업의 대응이 시급한 시점이다. 이 연구는 빅 데이터 활용기반 구축과 분석기술 개발에 도움을 주고자 빅 데이터 분석의 핵심기술동향을 분석하고 실천과제를 제시한다.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.191-204
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• 2024

This study analyzes the effects of manufacturing firms' R&D investment on sales according to global political economic uncertainty. The variables in this research include the firm's R&D investment, sales, which serves as an indicator of the firm's performance, and the Global Economic Policy Uncertainty (GEPU) index, which reflects situations of global political economic uncertainty. Panel data analysis is conducted by using a total of 96 quarters of data spanning 24 years from 2000 to 2023 based on manufacturing firms in the Wharton Research Data Services' Compustat Database. We study the impact of firm's R&D investment on sales by considering the Global Economic Policy Uncertainty index, which was relatively underestimated in previous research, as moderating variable, and present a new direction for research by analyzing the time lag effect. We suggest effective R&D investment strategy for firms.