• Journal of Korean Society of Archives and Records Management
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• v.20 no.4
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• pp.41-58
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• 2020

Although numerous studies have noted the importance of data sets in government information systems, the practical management of data sets has yet to be developed. Under these circumstances, the National Archives of Korea designated data set management as a major project in 2020, initiating full-scale management work. Despite these efforts, the records center, which will conduct management, expressed great concern for the new project. As such, this study identifies problems in managing data sets and searches for possible improvements through a comparison with existing public data projects by public institutions. In particular, the following materials were analyzed: laws, notices, guidelines, and publications issued by the ministries. Based on the results, several measures were proposed as part of an improvement plan for data set management: (1) the utilization of government functional classification as a reference, (2) the reorganization of the table, and (3) data linkage with related systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.168-181
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• 2023

This study analyzed the effects of traffic enforcement on accident reduction. The results revealed a significant reduction in both overall accidents (28.53%) and fatal accidents (39.44%). Notably, enforcement equipment targeting speed limits of 30 km/h and 50 km/h demonstrated similar accident reduction rates of 42.23% and 25.85%, respectively. However, variations were observed based on accident types and types of traffic violations. Therefore, it is evident that enforcement equipment yields distinct accident reduction effects depending on speed limits and types of traffic accidents. This finding underscores the potential for making informed policy decisions to enhance traffic safety measures.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.751-759
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• 2021

This study developed an artificial intelligence education program using localized public data as an educational method for improving computing thinking skills of elementary school students. According to the ADDIE model, the program design was carried out based on the results of pre-requisite analysis for elementary school students, and textbooks and education programs were developed. Based on localized public data, the training program was constructed to learn the principles of artificial intelligence using machine learning for kids and scratches and to solve problems and improve computational thinking through abstracting public data for purpose. It is necessary to put this training program into the field through further research and verify the change in students' computational thinking as a result.

• Information and Communications Magazine
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• v.29 no.11
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• pp.36-42
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• 2012

대부분의 빅 데이터 분석 결과는 문장으로 다듬어진 보고서로 결과를 제시하기보다는 비주얼라이제이션으로 결과를 보이고 보는 사람이 갖고 있는 배경지식과 통찰력에 따라 의미 있는 내용을 도출할 수 있도록 한다. 본 고에서는 공공 데이터와 비주얼라이제이션의 중요성을 알리기 위한 국회의원 투표 성향 분석 프로젝트를 소개한다. 우리나라 국민이라면 누구나 관심을 가질만한 국회의원의 성향을 파악하는 것을 세부 목표로 하고 18대 국회의원의 본 회의 회의록으로부터 투표 데이터를 수집하여 유사한 투표성향을 가진 국회의원을 가깝게 배치하여 누구나 쉽게 의원간의 유사성을 알 수 있도록 하였다. 데이터 수집부터 분석모델 개발과 웹 페이지를 만들기까지의 프로젝트 진행과정에서 모델의 수정이 필요했으며 왜 그러한 선택을 했는지를 설명한다.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.10-17
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• 2022

Recently, with the issue of the 4th Industrial Revolution, many services using big data have been developed. Accordingly, studies have been conducting to utilize public data, which is considered as the most valuable data among big data. In this paper, we design and implement a food price information analysis system based on public big data. The proposed system analyzes the collected food price-related data in various forms from various sources and classifies them according to characteristics. In addition, the proposed system analyzes the factors affecting the price of food through big data analysis techniques and uses them as data to predict the price of food in the near future. Finally, the proposed system provides the user with the analyzed results through data visualization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.367-370
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• 2022

본 연구는 코로나19 발생 전·후 공공의료를 둘러싼 사회적 인식변화를 뉴스빅데이터를 통해 파악하고자 시도되었다. 뉴스빅데이터는 코로나19 확진자가 처음 발생한 2020년 1월을 기준으로 나누었으며, 코로나19 발생 이전(2018년 1월~2019년 12월, 총 24개월) 40,834건과 코로나19가 발병 이후(2020년 1월~2021년 12월, 총 21개월) 61,761건이었다. 수집된 빅데이터는 R 4.1.1 for Windows를 활용하여 단어 빈도 분석, 연관규칙분석을 실시하였다. 연구결과, 코로나19 발생 전후 뉴스기사에서 공공의료를 둘러싼 핵심어를 비교할 때 코로나19 발생 후에 발생 전보다 큰 폭으로 상승한 단어는 '확산'(664%), '대응'(658%), '의사'(518%), '상황'(504%), '공공병원'(486%), '의료진'(455%), '확충'(324%), '인력'(305%), '어려움'(272%), '정부'(247%)순으로 나타났다. 코로나19 발생 전후 공공의료를 둘러싼 키워드의 연관규칙 분석을 통해서 의료의 패러다임이 일자리 산업에서 감염증 대응을 위한 보건의료로 전환되는 것을 알수 있었다.

• Electronics and Telecommunications Trends
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• v.28 no.4
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• pp.118-131
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• 2013

최근 과학 및 IT 패러다임은 HW(과거) 및 SW(현재) 중심에서 '데이터 빅뱅'을 활용하여 정치 사회 경제 등 제반 이슈와 연계된 분석 예측으로 진화 중이다. 국가안전 및 위험관리, 의료, 교육, 복지, 환경 등 사회 전반에 걸쳐 공공부문에서의 빅데이터 활용 가능성이 높아지고 있다. 공공정보 자체뿐만 아니라 민간정보와의 통합 분석을 통해 효과적인 맞춤형 정책과 새로운 지식정보 서비스 제공이 가능하기 때문에 주요 선진국은 공공정보의 적극 활용을 위해 다양한 정책을 경쟁적으로 추진해 오고 있다. 공공정보의 민간활용 촉진은 정보 제공자인 공공기관뿐만 아니라, 이를 활용하는 민간 사업자, 관련 서비스 사용자 모두에게 경제적 비경제적 가치를 제공할 수 있다. 따라서, 본 연구를 통해 국내 공공정보 민간활용 산업의 경제적 파급효과를 추정하고, 중요한 전 후방 연결 산업들을 파악하고자 한다.

• Informatization Policy
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• v.24 no.1
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• pp.30-47
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• 2017

The Korean government developed an organizational maturity model for public data quality management based on international standards to evaluate the data quality management level of public organizations, However, as the model has too many indicators to apply on the site, a new model with reduced number of indicators is proposed in this paper. First, the number of processes is reduced by integrating and modifying the processes of the previous model. Second, a new maturity evaluation method is proposed based on capability levels focused on the activity, not on the process. Third, the maturity level of public data quality management is represented by five discrete levels or real values of 1 through 5. Finally, characteristics of the proposed model are compared with those of the previous model.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.1-6
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• 2021

This study designed an artificial intelligence education program using localized public data as an educational method for improving computational thinking in elementary school students. Program design and development was carried out based on the results of pre-requisite analysis on elementary school students according to the ADDIE model. Based on localized public data, the program was organized to learn the principles of artificial intelligence by utilizing "Machine Learning for Kids" and "Scratch" and to solve problems and improve computational thinking skills through abstracting public data for purpose.Through subsequent research, it is necessary to put this education program into the field and verify the change in students' computational thinking as a result.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.3
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• pp.79-99
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• 2020

The purpose of this study is to investigate the model of cluster analysis techniques for grouping public libraries and analyze their characteristics. Statistical data of public libraries of the National Library Statistics System were used, and three models of cluster analysis were applied. As a result of the study, cluster analysis was conducted based on the size of public libraries, and it was largely divided into two clusters. The size of the cluster was largely skewed to one side. For grouping based on size, the ward method of hierarchical cluster analysis and the k-means cluster analysis model were suitable. Three suggestions were presented as implications of the grouping method of public libraries. First, it is necessary to collect library service-related data in addition to statistical data. Second, an analysis model suitable for the data set to be analyzed must be applied. Third, it is necessary to study the possibility of using cluster analysis techniques in various fields other than library grouping.