Earticle

본 논문은 디지털 기술의 발전으로 인해 가능해진 메타버스 환경에서 문화유산을 보존하고 전파하기 위한 새 로운 접근 방식을 제안한다. 메타버스는 현실과 가상 세계를 통합하는 공간으로 이를 통해 현실 세계의 박물 관과 비슷한 경험을 메타버스 환경에서 제공할 수 있게 되었다. 이러한 기술은 사용자들에게 더욱 몰입적이고 상호작용적인 방법으로 문화유산을 탐구할 수 있는 기회를 제공한다. 본 연구에서는 문화유산 공공데이터 소 스로부터 3D 모델을 추출하고, Unity 엔진을 사용하여 이러한 모델들을 통합하여 메타버스 박물관 환경을 개 발하는 방법을 다룬다. 공공데이터란 공공기관이 생성하거나 관리하는 데이터 또는 정보로서, 다양한 형태(텍 스트, 그림, 이미지, 비디오, 오디오 등)의 모든 데이터 또는 정보를 의미한다. 본 연구는 Unity를 활용하여 문 화재청에서 제공하는 3D 데이터를 활용한 메타버스 박물관을 구축하는 과정과 결과를 제시함으로써, 문화유 산 홍보와 교육을 위한 혁신적인 방법을 제안하고자 한다. 이를 통해 사용자들은 문화유산을 더욱 다양하고 흥미로운 방식으로 체험하고 학습할 수 있으며, 메타버스를 통해 문화유산의 액세스와 보존을 증진시킬 수 있 는 가능성을 높일 것으로 예상된다. 미래사회에는 메타버스를 이해하고, 그 공간에서 소통하고 교류하면서 새 로운 콘텐츠를 만들어내는 능력이 필요하다.

This paper proposes a novel approach to preserving and disseminating cultural heritage in the metaverse environment made possible by the advancement of digital technology. The metaverse integrates the real and virtual worlds, enabling experiences similar to real-world museums within the metaverse environment. This technology provides users with more immersive and interactive means to explore cultural heritage. This study addresses the methodology for extracting 3D models from public cultural heritage data sources and integrating these models using the Unity engine to develop a metaverse museum environment. Public data refers to data or information generated or managed by public agencies, encompassing various forms such as text, graphics, images, video, audio, and more. By utilizing 3D data provided by the Cultural Heritage Administration, this research aims to construct a metaverse museum, showcasing the process and outcomes. Through this, users can experience and learn about cultural heritage in more diverse and interesting ways. It is expected that the metaverse will increase the possibility of improving access to and preservation of cultural heritage. Future society requires the ability to understand the metaverse and create new content while communicating and interacting in that space.

National and local governments around the world have been allowing access to administrative data to increase transparency, motivate civic engagement of citizens, and improve collaboration between the public and the government. This study reviews and classifies existing literature on open government data (OGD). To create a structure to organize the existing studies, the researchers devised a framework based on socio-technical theory and summarized the significance of studies along four major points: (1) readiness, (2) implementation, (3) emerging effects, and (4) actors of open data. Studies in OGD have been growing steadily in the recent years because of the rapid development of adoptable technologies that have enabled easier access to government data. Nonetheless, an examination of existing research not only shows a disparity in research and development of OGD across countries in the Open Government partnership program but also reveals pertinent issues that have arisen in different stages of the OGD initiative. The growing number of studies and expanding body of knowledge show the importance of organizing existing literature. This step is timely and significant to map out the current breadth and depth of OGD research. According to existing research, current open governments fall short in encouraging citizen participation and collaborations among citizens and the government. This study pointed out how studies on OGD actors might be the reason as well as the solution to existing findings. This paper aims to provide a framework for organizing OGD studies, present the status of OGD research, and provide recommendations on current gaps that must be addressed.

코로나 19로 인한 사회적 거리두기는 비대면화와 디지털 전환을 가속화 시키며 전 산업 분야에 영향을 미치고 있다. 이러한 거대한 사회적 전환점에서 교육 분야는 홈스쿨링 수요가 급증하였고 ICT기술을 활용하여 교 육의 효과를 높이는 에듀테크의 관심이 커지고 있다. 또한 최근 정부가 발표한 디지털 뉴딜 정책 따라 디지털 대 전환이 예고되고 그 변화의 물결로 누구나 양질의 공공데이터를 활용할 수 있는 환경이 조성되고 있다. 본 논문은 날로 커지는 온라인 콘텐츠 시장에서 공공데이터를 활용한 교육용 어플리케이션 플랫폼 제안을 통해 교육 기회의 불균형을 해소하고 새로운 디지털콘텐츠의 가능성을 모색하고자 하였다. 데이터 댐 구축에 따른 풍부한 공공데이 터 개방과 디지털 기술은 앞으로 누구나 쉽고 편리하게 교육 콘텐츠 서비스를 제공하고 이용할 수 있는 마중물이 될 것으로 기대된다.

The social distancing caused by Corona 19 is affecting all industries and it’s accelerating non-face-to-face and digital transformation. At this huge social turning point, the demand for homeschooling in the field of education is increased and the edutech's interest in enhancing the effectiveness of education using ICT technology is growing. In addition, according to the Digital New Deal policy announced by the government recently, a great digital transformation is foretold, and the wave of change is creating an environment in which anyone can use high-quality Open data. This paper aims to resolve the imbalance of educational opportunities and explore the possibility of new digital contents by proposing an educational application platform using open data in the growing online content market. The openness of abundant open data and digital technology by the construction of a data dam is expected to become a priming water that anyone can easily and conveniently provide and use educational content services.

본 연구는 실생활 문제해결을 위한 ‘공공데이터 활용 융합 수업’을 계획하고 모의수업을 진행하는 과정에서 예비 교사들의 실제적인 수업 역량을 기르는 것을 목표로 한다. 구체적으로, ‘공공데이터 활용 융합 수업’ 계획서를 맥락적 영역과 통계적 영역으로 구분하여 분석함으로써 예비 교사의 공공데이터 활용과 연계한 통계 교육의 내실화된 현장 실행 방안을 탐색하고, 통계적 소양 함양을 위한 시사점을 제공하고자 한다. 이를 위해 K대학 사범대학 수학교육과 학생들을 대상으로 실생활 문제 상황을 수업 맥락으로 활용하는 '공공데이터 활용 융합수업'을 설계하도록 하였으며, 이를 통해 5개의 수업계획서와 예비교사의 발문을 수집·분석하였다. 연구 결과, 예비수학교사들이 공공데이터를 융합수업에 적용함으로써 실제 주변의 쟁점이 되는 비표준화 데이터를 추출하는 경험을 얻고, 수업실천 역량에 대한 시사점을 도출할 수 있었다. 이는 공공데이터를 수학 수업에 융합하려는 예비교사들의 노력이 단순한 기술적 수업 전문성을 넘어서는 새로운 교사교육의 시도임을 보여준다. 향후 인공지능 기반 미래 교육과 AIDT 디지털 교실 혁명의 요구에 기여할 것이다.

The purpose of this study is to develop the practical teaching capabilities of prospective teachers by planning statistical mathematics lessons that solve real-life problems using the Open Data portal. Specifically, the purpose is to strengthen the statistical literacy of prospective teachers through the development of statistics classes using the Open Government Data portal. The research method involved implementing teacher education. Additionally, five lesson plans and the questions used in the mock lessons were collected and analyzed. As a result, the teacher education program can be used as educational material for developing expertise in improving school mathematics. By addressing real-life problem situations and issues, it was possible to provide prospective teachers with a new perspective on changing societal problems. Accordingly, the efforts of pre-service teachers to integrate Open Data into mathematics lessons can be seen as an initiative in teacher education that goes beyond the development of technical teaching expertise. It can be concluded that these efforts may contribute to the practical teaching competencies necessary to adapt to the demands of the digital classroom revolution through future education based on artificial intelligence and the utilization of AIDT.

최근 강력 범죄의 발생 빈도가 증가하면서 사회적 불안감이 고조되고 있으며, 기존의 사후 대응 중심의 치안 시스템만으로는 범죄를 효과적으로 예방하는 데 한계가 드러나고 있다. 이에 따라 사전 예측을 통한 범죄 예방의 필요성이 대두되었고, 공공데이터를 활용한 범죄 예측 모델은 이를 해결할 수 있는 유망한 접근법으로 주목받고 있다. 특히, 빅데이터와 인공지능(AI) 기술의 발전은 범죄 발생 가능성을 정교하게 예측하고 실시간 대응을 가능하게 할 잠재력을 제공한다. 그러나 국내에서 여러 영역의 이종데이터를 확보하여 예측 기반 모델에 적용하기 위해서는 데이터의 지역적 한정성이나 수집 주기의 비효율성 등 실무적 한계뿐 아니라, 개인정보가 포함된 데이터의 활용상 제약, 데이터의 권리 주체와의 권리 처리 문제, 법률에 규정된 행정정보 성격의 데이터 처리에 대한 법령상의 제약 등과 같은 여러 법적 한계가 존재한다. 이는 효과적인 범죄 예측 모델을 개발하고 서비스화하여 적용하는 데 있어서 많은 어려움을 초래한다. 이에 본 연구는 법적 권한 확보가 비교적 용이한 공공데이터를 중심으로 하는 강력 범죄 예측 서비스 모델을 개발하고자 한다. 이를 위해 최신 연구 동향과 성공적인 해외 사례를 분석하고, 본 모델의 개발에 필요한 수요데이터에 대한 법적 근거와 그 활용을 제고시키기 위한 입법적 대안을 제안함으로써 한국적 상황에 최적화된 범죄 예측 서비스 모델을 제안한다. 또한, 데이터 수집과 활용의 투명성 확보, 개인정보 보호법 준수를 위한 방안을 함께 제시함으로써, 지속 가능한 범죄 예방 체계의 구축을 목표로 한다.

As violent crime rates increase, the limitations of traditional crime prevention systems focused on post-incident responses have become evident. Predictive crime prevention using public data, combined with advancements in big data and AI technologies, offers a promising alternative by enabling precise crime forecasting and real-time responses. However, in South Korea, integrating diverse data sources into predictive models faces challenges such as regional data constraints, inefficient data collection cycles, restrictions on using personal information, ownership and rights issues, and legal limitations regarding administrative data usage. These obstacles hinder the development and application of effective crime prediction services. This study focuses on developing a crime prediction model based on public data, where securing legal access is relatively straightforward. By analyzing recent research trends and successful international examples, the study identifies key legislative measures to enhance data utilization and proposes a crime prediction model optimized for Korea’s specific context. Additionally, it suggests strategies for ensuring transparency in data collection, compliance with privacy laws, and building a sustainable crime prevention system.

최근 강력 범죄의 발생 빈도가 증가함에 따라 사회적 불안감이 높아지고 있으며, 기존의 사후 대응 중심의 치안 시스템만으로는 한계가 있어, 사전 예측을 통한 범죄 예방의 필요성이 대두되고 있다. 행정데이터를 활용한 범죄 예측 모델은 이러한 문제 해결에 기여할 수 있는 유망한 방법으로 주목받고 있다. 하지만, 해외와 달리 국내의 경우 제대로 활용할 수 있는 서비스 모델의 부재와 데이터 활용 등에 있어서의 법률적 문제가 존재하고 있어, 이를 적용하는데 어려움을 겪고 있다. 본 연구는 행정데이터를 활용하여 강력 범죄를 사전에 예측하고 예방하기 위한 서비스 모델을 개발하고자 한다. 이를 위해 최신 연구들을 기반으로 범죄 예측 모델의 발전 동향 및 해외의 범죄 알림 서비스 사례를 분석하고, 국내 데이터 활용 현황과 법률적 고려사항을 검토하여 국내 적용 가능성을 제시한다.

Data plays an ever greater part in the tourism industry. While the platform-based sharing of open public data, private-sector intermediary platforms, and the use of social media to understand consumer trends are already well recognized, more potential for innovation exists in sharing private data among organizations in Smart Tourism Destinations. Research into the factors enabling and hindering coopetition in this kind of data sharing platforms is still in the nascent stage of development. Our case study of Sapporo, a major Japanese city endeavouring to create itself as a Smart Tourism Destination, sheds light on the initial approaches to involve organizations to such a data sharing agreement. Founding on seven interviews with ten participants of Sapporo Smart City project organization (SARD), we derived enablers and impediments that promote coopetition in data sharing as part of Smart Tourism Destination development. We also present practical recommendations and future research opportunities for such initiatives.

This study aims to derive basic living service facility items in rural areas and construct related spatial data. To do this, a literature review on the laws and systems related to the residential environment and services in rural areas, rural spatial planning, and the ‘Rural Convention’ strategic plan reports for the Jeolla and Gyeongsang Region in 2021 was conducted. Primary data collection and review on the list of basic living service items in rural areas derived from the analysis were conducted. After data collection, 12 sectors and 44 types of rural basic living service items were derived; the data selection was carried out based on the clarity of the subject of data management, whether it was established nationwide, whether it was disclosed and provided, whether it was periodically updated, and whether it was an underlying law. Afterwards, data on the derived rural basic living service items were constructed. Afterwards, spatial data on the derived rural basic living service items were constructed. Because open data provided through various institutions were employed, data structure unification such as data attribute values and code names was needed, and abnormal data such as address errors and omissions were refined. After that, the data provided in text form was converted into spatial data through geocoding, and through comparative review of the distribution status of the converted data and the provided address, spatial data related to rural basic living services were finally constructed for about 540,000 cases. Finally, implications for data construction for diagnosing rural living areas were derived through the data collection and construction process. The derived implications include data unification, data update system establishment, the establishment of attribute values necessary for rural living area diagnosis and spatial planning, data establishment plan for facilities that provide various services, rural living area analysis method, and diagnostic index development. This study is meaningful in that it laid the foundation for data-based rural area diagnosis and rural planning, by selecting the basic rural living service items, and constructing spatial data on the selected items.

최근 에너지 산업은 에너지사용량 감축을 위해 신·재생에너지 공급정책을 시행하고 있다. 본 연구는 신·재생에너지 수요 증가에 대비하기 위한 지열에너지 시스템 공급 촉진에 도움이 될 수 있는 데이터베이스를 구축하고, 데이터베이스를 활용한 지열에너지 시스템 설치 가능성 평가 방법 개발을 목표로 한다. 연구에 사용한 데이터는 국가기관에서 제공하는 공 공데이터를 사용하여 데이터의 신뢰성을 확보하였다. 지열에너지 시스템 설치 가능성 평가를 위해 필요한 정보를 취득하여 전용 데이터베이스를 구성하였고, 데이터베이스의 정보를 활용하여 지열정 용량을 산정하는 방법을 연구하였다. 본 연구는 추후 지역 환경평가 기준을 정립하고, 다른 신재생에너지에 대한 정보를 추가하여 신재생에너지 보급 활성화에 기여할 수 있는 연구를 진행할 예정이다.

Recently, the energy industry is implementing renewable energy supply policy to reduce energy consumption. The purpose of this study is to build a database that can help promote the supply of geothermal energy system to prepare for the increase of renewable energy demand and to develop a method to evaluate the possibility of geothermal energy system installation by using database information. The data used in the study was reliable using open data provided by national agencies. We obtained information necessary for the possibility of geothermal energy system installation, constructed a dedicated database, and studied the method of calculating the geothermal well capacity by using the database information. In the future, this study will establish a local environmental evaluation standard and add information on other renewable energy to contribute to the activation of renewable energy supply.

공공데이터 개방은 국가의 흥망을 결정하는 중요한 과제로 인식되고 있다. 글로벌 경제 위기 이후에 경제 활성화에 고심하고 있는 각국 정부는 높은 경제적 효과를 창출하고자 신경제 성장 동력으로 공동데이터 공급을 확대하고 있다. 따라 서 전 세계적으로 급속히 전파되고 있는 공공데이터 민간 활용은 일자리 창출과 경제 활성화를 실현하기 위해 필연적으로 추진해야 하는 신경제 성장 동력으로 인식됨으로, 민간의 공공데이터 활용을 위한 이용의도에 미치는 영향에 관한 연구가 절실히 요구되는 시점이다. 이 연구는 다음과 같은 목적을 달성하고자 시도했다. 첫째, 공공데이터를 사용할 의사에 대한 시스템 품질, 정보 품질, 정보 보안, 사회적 영향, 공공 기관의 혁신 및 지원과 같은 독립 변수의 영향을 조사했다. 둘째, 독립적인 변수와 사용 의도(종속 변수)에 대한 중재 변수인 인지된 사용용이성(PEU)과 인지된 유용성(PU)의 영향을 조사 했다. 셋째, 활용 유형, 공공데이터 이용 빈도, 중재 변수로서의 직업 및 전자 정부 서비스 이용 빈도를 선정하여 독립 변수와 종속 변수(민간 부문에서의 이용 의도)와의 관계에 미치는 영향을 조사하였다. 연구 결과는 공공데이터 서비스를 활용하기 위한 전략 개발에 유용할 것으로 기대된다.

The opening of public data has been perceived as a critical factor in deciding a country's rise or fall. Since the global economic crisis, countries around the globe have expanded the supply of public data as a new growth engine to create significant economic effects. As a result, there has been a rising demand for a study on the influence of public data in the private sectors. This study attempted to achieve the following objectives. First, the effects of independent variables-system quality, information quality, information security, social influence, innovation and assistance by the public organization- on the intention to use the public data was examined. Second, the effects of the mediating variables - Perceived Ease of Use (PEU) and Perceived Usefulness (PU) – on the independent variables and intention to use (dependent variable) were investigated. Third, after selecting utilization type, frequency of public data usage and frequency of occupational & e-government service usage as moderating variables, their effects on the relationship between the independent variables and dependent variable (intention to use them in the private sector) were studied. It is expected that the study results would be useful in developing strategies aimed to utilize public data in the private sectors.

소셜 미디어 데이터를 통해 파급되는 형태를 분석하여 국내 외 정치, 경제, 보건, 사회 문화현상을 대응하고자 하는 연구가 활발히 진행 중이다. 본 연구는 한국인이 가장 많이 사용하는 검색 서비스인 검색 정보를 알 수 있는 네이버 트렌드와 소셜 데이터인 네이버 블로그, 네이버 카페와 Open Data(API)를 사용하고 기상청의 온도, 습도 데이터를 사용하였다. 사람의 감성을 나타내는 감정 어휘와 감각을 표현하는 감각어휘 중 미각 어휘를 분석하여 대중의의 감성 활동 변화를 연구하였다. 적합도 검증과 계층적 군집분석으로 군집의 개수를 정하여 비 계층적 군집분석으로 군집화 하였다. 군집분석 결과 8개의 군집으로 군집화되어 감성어휘를 알 수 있었다. 판별분석에 의하면, 군집분석에서 결정된 8개의 그룹은 98.9% 정확성을 갖는 것으로 나타났다. 본 연구에서 연구한 감성 활동 변화는 온도와 습도에 의해 감성 활동을 예측 할 수 있어 감성을 공유하고 대중의 기분을 파악하여 서로 공감대를 형성 할 수 있다.

Many researches about the effect on politics, economics and Sociocultural phenomenon using the social media are in progress. Authors utilized NAVER Trend most famous web browsing service in korea, NAVER Blog social media, NAVER Cafe service and Open Data(API) and also used temperature, humidity index data of Korea Meteorological Administration. This study analyzed a change of the public's emotion in korea using Cluster analysis of vocabulary of taste among its of feelings and senses. K-means clustering was followed by decision of the number of groups which was used Chi-square goodness of fit test and ward analysis. Eight groups was made and it represented sensitive vocabulary. By Discriminant analysis, eight groups decided by Cluster analysis has 98.9% accuracy. The change of the public's emotion has capability to predict people's activity so they can share sensibility and a bond of sympathy developed between them.

This work assesses the quality of Internet of Things data not only as an intrinsic quality on how well it represents the related phenomenon but also, on how much information it contains to educate an artificial entity. The quality metrics here proposed are tested with real datasets. Also, they are implemented on OpenCPU, so the open data repositories can use them off-the-shelf to rate their datasets without computational cost and minimum human intervention, making them more attractive to potential users and gaining visibility and impact.

The present study specifies the historical development of data sharing practices in three disciplines?oceanography, ecology, and genomics?along with the evolving progress of movements?e-Science, cyberinfrastructure, and open science?that expedite data sharing in more diverse disciplines. The review of these disciplinary data-sharing practices and the movements suggests opportunities and challenges that would serve as a basis for implementing data-sharing practices. The increasing need for large-scale and interdisciplinary research provides momentum for initiating data sharing. In addition, the development of data repositories and standards for metadata and data format facilitates data sharing. However, challenges need to be addressed, in regard to conflicting issues of patenting data, concerns about privacy and confidentiality, and informed consent that adequately enables data sharing. It is also necessary to consider the needs of the various stakeholders involved in data sharing to incentivize them to improve its impact.

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Using the network analysis method, this study investigates the communication structure of Open Data on the Twitter sphere. It addresses the communication path by mapping influential activities and comparing the contents of tweets about Open Data. In the years 2015 and 2016, the NodeXL software was applied to collect tweets from the Twitter network, containing the term "opendata". The structural patterns of social media communication were analyzed through several network characteristics. The results indicate that the most common activities on the Twitter network are related to the subjects such as new applications and new technologies in Open Data. The study is the first to focus on the structural and informational pattern of Open Data based on social network analysis and content analysis. It will help researchers, activists, and policy-makers to come up with a major realization of the pattern of Open Data through Twitter.

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

While open government data (OGD) is increasingly recognized as a critical resource for economic growth and data-driven innovation, methods for proactively evaluating the potential utilization of these datasets remain underdeveloped. This study addresses this gap by investigating two key methodological questions: first, whether automated machine learning (AutoML) is an appropriate tool for measuring and evaluating OGD utilization, and second, how the composition of training data affects the performance of models designed to predict such utilization. This research specifically compares the efficacy of two distinct data strategies: models trained on integrated datasets spanning multiple domains versus those trained on domain-specific datasets. Using metadata from the South Korean government’s extensive OGD portal, this study employs AutoML to systematically build and evaluate predictive models under these different training conditions. The findings reveal that the training data strategy is a critical determinant of predictive accuracy, with the integrated-domain approach frequently yielding superior performance over domain-specific models. This research provides empirical evidence on the impact of data integration strategies in this context and establishes a methodological framework for the prospective assessment of OGD value, offering a more robust alternative to traditional retrospective evaluation metrics.

Interest in open data is continuing to grow around the world. In particular, open government data are considered an important element in securing government transparency and creating new industrial values. The South Korean government has enacted legislation on opening public data and provided diversified policy and technical support. However, there are also limitations to effectively utilizing open data in various areas. This paper introduces an administrative district knowledge model to improve the sharing and utilization of open government data, where the data are semantically linked to generate a knowledge graph that connects various data based on administrative districts. The administrative district knowledge model semantically models the legal definition of administrative districts in South Korea, and the administrative district knowledge graph is linked to data that can serve as an administrative basis, such as addresses and postal codes, for potential use in hospitals, schools, and traffic control.

Since the advent of Linked Data (LD) as a method for building webs of data, there have been many attempts to apply and implement LD in various settings. Efforts have been made to convert bibliographic data in libraries into Linked Data, thereby generating Library Linked Data (LLD). However, when memory institutions have tried to link their data with external sources based on principles suggested by Tim Berners-Lee, identifying appropriate vocabularies for use in describing their bibliographic data has proved challenging. The objective of this paper is to discuss the potential role of Linked Open Vocabularies (LOV) in providing better access to various open datasets and facilitating effective linking. The paper will also examine the ways in which memory institutions can utilize LOV to enhance the quality of LLD and LLD-based ontology design.

본 논문에서는 Open Government Data(이하 OGD) 제공 서비스를 위한 API 설계 및 구현 방 안을 다룬다. API를 제공하는 방식은 가장 많이 사용되는 RESTful 방식을 사용했다. OGD의 표현의 성숙도를 표현하기 위해 5-Star 모델을 사용하고, 구현기술인 RESTful API의 성숙도를 표시하기 위 해서는Richardson model 을 이용하였다. 5-Star 수준별 구현을 위해 사용할 수 있는 Richardson model의 수준들을 분석하였고 이들의 구현 방안도 제시 하였다.

In this paper, we discuss issues in designing and implementing an Application Programming Interface(API) for Open Government Data (OGD) service. To provide API, we used RESTful methods since it is most often used currently. For the maturity model in representing OGD, we used the 5-Star model and for the maturity model in implementation technology, we used Richardson maturity model. We then discuss issues in providing API for each level in 5-Star model by using levels in Richardson’s model. We also presented implementation methodology for each level in 5-Star model.