• Digital Contents
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• no.4 s.143
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• pp.101-101
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• 2005

한국데이터베이스진흥센터는 제니시스 기술과 공동 주관, 한국CA 후원으로 3월 17일 한국과학기술단체총연합회 대강당에서 ‘데이터 모델링 및 DB설계 핵심 기법 30題세미나’를 개최했다. 데이터 모델링 및 DB설계를 주제로 한 이 세미나에서는 EA도구의 이해 및 전략적 적용, 데이터 모델링 도구를 통한 활용사례 등 다양한 DB 실무 기법을 다뤘다.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.1
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• pp.365-385
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• 2020

In this study, in order to analyze the current status of the data science curriculum in Korea as of October 2019, we conducted an analysis of the prior studies on the curriculum in the data science field and the competencies required for data professional. This study was conducted on 80 curricula and 2,041 courses, and analyzed from the following perspectives; 1) the analysis of the characteristics of data science domain, 2) the analysis of key competencies in data science, 3) the content analysis of the course titles. As a result, data science program in Korea has become a research-oriented professional curriculum based on an academic approach rather than a technical, vocational, and practitional view. In addition, it was confirmed that various courses were established with a focus on statistical analysis competency, and interdisciplinary characteristics based on information technology, statistics, and business administration were reflected in the curriculum.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.655-662
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• 2023

Big data research plays a role of supplementing existing social science research methods. If the survey and experimental methods are somewhat inaccurate because they mainly rely on recall memories, big data are more accurate because they are real-time records. Social science research so far, which mainly conducts sample research for reasons such as time and cost, but big data research analyzes almost total data. However, it is not easy to repeat and reproduce social research because the social atmosphere can change and the subjects of research are not the same. While social science research has a strong triangular structure of 'theory-method-data', big data analysis shows a weak theory, which is a serious problem. Because, without the theory as a scientific explanation logic, even if the research results are obtained, they cannot be properly interpreted or fully utilized. Therefore, in order for big data research to become a methodological innovation, I proposed big thinking along with researchers' efforts to create new theories(black boxes).

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.465-486
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• 2024

The purpose of this study is to identify the current status of infrastructure and services for analyzing research data for research data managers at government-funded research institutions under the National Research Council for Science and Technology (NST) who will actually use the Korea Research Data Commons (KRDC), which is being developed by the Korea Institute of Science and Technology Information (KISTI) and to investigate the perceptions of research data managers related to the establishment of KRDC system. For the study, we conducted a survey targeting 24 government-funded research institutes, excluding KISTI, and interviewed research data managers from 9 of the 15 institutions surveyed who agreed to follow-up interviews. As a result of the survey, most institutions were providing related services, and their willingness to introduce an integrated analysis framework for the use of research data and provide a system for using externally released analysis software was also high. Meanwhile, when we investigated the external disclosure status of each institution's analysis services through follow-up interviews, only a minimal number of institutions were disclosing them to the outside world. The findings reveal that there is a demand to utilize analysis infrastructure and services when provided through the framework. However, it is difficult to disclose and share the analysis resources held by each organization. In order to establish the KRDC system, it is essential to share research sites' analysis infrastructure and services, and in addition, changes in the perception of research sites and institutional changes are necessary. Furthermore, there is a need to establish policies that consider the system's convenience, security, and compensation system raised in the follow-up interviews.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.904-913
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• 2020

As the data volumes and complexity rapidly increase, data-intensive science handling large-scale scientific data needs to investigate new techniques for intelligent storage and data distribution over networks. Recently, Named Data Networking (NDN) and data-intensive science communities have inspired innovative changes in distribution and management for large-scale experimental data. In this article, analysis on NDN testbeds for large-scale scientific data such as climate science data and High Energy Physics (HEP) data is presented. This article is the first attempt to analyze existing NDN testbeds for large-scale scientific data. NDN testbeds for large-scale scientific data are described and discussed in terms of status, NDN-based application, and features, which are NDN testbed instance for climate science, NDN testbed instance for both climate science and HEP, and the NDN testbed in SANDIE project. Finally various issues to prevent pitfalls in NDN testbed establishment for large-scale scientific data are analyzed and discussed, which are drawn from the descriptions of NDN testbeds and features on them.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.430-432
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• 2022

With the recent increase in interest in the open science movement in science and technology fields such as open access, open data, and open source, the movement to share and utilize publicly funded research products is materializing and revitalizing. In line with this trend, many efforts are being made to establish and revitalize a system for sharing and utilizing research data, which is a key resource for research in Korea. These efforts are mainly focused on collecting research data by field and institution, and linking it with DataON, a national research data platform, to search and utilize it. However, developed countries are building a system that can share and utilize not only such research data but also various types of R&D-related computing resources such as IaaS, PaaS, SaaS, and MLaaS. EOSC (European Open Science Cloud), ARDC (Australian Research Data Commons), and CSTCloud (China S&T Cloud) are representative examples. In Korea, the Korea Research Data Commons (KRDC) is designed and a core framework is being developed to facilitate the sharing of these computing resources. In this study, the necessity, concept, composition, and future plans of KRDC are introduced.

• Journal of Information Management
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• v.36 no.1
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• pp.33-56
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• 2005

This paper proposes some guidelines for Korean standards for statistical metadata to share on the Internet. The construction includes the minimum metadata set needed in the international standards based on the report and Korean Statistics Office. In result, 29 categories were selected from the SDMX and SDDS’consistency items and 14 categories from Dublin Core. Overall, 43 international statistics metadata was completed.

• Proceedings of the Korean Society for Information Management Conference
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• 2016.08a
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• pp.67-70
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• 2016

빅데이터 시대로 변화함에 따라 도서관 및 정보서비스 분야에서도 데이터 분석에 대한 중요성이 점차적으로 증대되고 있다. 본 연구는 도서관 분야에서의 데이터 분석활용 현황 및 분석서비스에 대한 인식수준을 파악하고, 이를 바탕으로 데이터 분석 기반의 도서관 운영을 지원할 수 있는 빅데이터 분석 서비스 개선방안을 모색하고자 하였다. 먼저, 도서관 분야 데이터 분석 교육 전후 인식조사를 토대로 현재 데이터 분석현황 및 인식변화를 분석하였다. 또한 개인적 특성과 분석서비스 인식과의 관계를 분석하였고, 추가적으로 인식수준이 교육 및 분석서비스 만족도에 미치는 영향에 대해 살펴보았다. 분석결과를 기반으로 향후 데이터 분석 교육 및 분석서비스의 발전방향을 제시하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.248-251
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• 2020

전 반도체 제조 및 검사 공정 과정을 자동화하는 스마트 팩토리의 실현에 있어 제품 검수를 위한 검사 장비는 필수적이다. 하지만 딥 러닝 모델 학습을 위한 데이터 처리 과정에서 엔지니어가 전체 웨이퍼 영상에 대하여 결함 항목 라벨을 매칭하는 것은 현실적으로 불가능하기 때문에 소량의 라벨 (labeled) 데이터와 나머지 라벨이 없는 (unlabeled) 데이터를 적절히 활용해야 한다. 또한, 웨이퍼 영상에서 결함이 발생하는 빈도가 결함 종류별로 크게 차이가 나기 때문에 빈도가 적은 (minor) 결함은 잡음처럼 취급되어 올바른 분류가 되지 않는다. 본 논문에서는 소량의 라벨 데이터와 대량의 라벨이 없는 데이터를 동시에 활용하면서 결함 사이의 발생 빈도 불균등 문제를 해결하는 점진적 데이터 평준화 (progressive pseudo-labeling balancer)를 제안한다. 점진적 데이터 평준화를 이용해 분류 네트워크를 학습시키는 경우, 기존의 테스트 정확도인 71.19%에서 6.07%-p 상승한 77.26%로 약 40%의 라벨 데이터가 추가된 것과 같은 성능을 보였다.