• Proceedings of the Korean Society for Information Management Conference
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• 2012.08a
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• pp.63-68
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• 2012

공공데이터베이스는 공무상의 활용뿐만 아니라 민간의 창의성과 결합할 경우 새로운 비즈니스와 일자리를 창출할 수 있는 잠재력을 가지고 있다. 미국과 영국 등 해외 주요국들은 공공정보 재사용의 가치를 깨닫고 공공데이터를 Linked Data화하는 작업을 진행하고 있다. Linked Data는 웹에서 자유롭게 데이터를 개방하여 연계할 수 있도록 하는 네트워크 기술이다. 본 고에서는 국내 공공정보의 개방과 재사용을 지원하기 위한 방안으로 Linked Data 구축을 제안하고, 이를 위해 데이터, 시스템, 서비스의 3가지 측면에서 표준화 방안을 제안하였다. 데이터 표준화 측면에서는 데이터 표현 및 접근에 관한 표준을 준수하고, 표준화된 데이터모델과 데이터구조에 대한 정보와 함께 완전하게 수요자 입장에서 배포해야 한다. 특히 데이터의 상용화나 재가공에 아무런 제약이 없는 완전한 공개를 원칙으로 해야 한다. 시스템 표준화 측면에서 Linked Data 플랫폼은 트리플 스토어, 원데이터의 트리플 변환기, 그리고 추론기로 구성되어야 한다. 서비스 표준화 측면에서는 Linked Data를 이용자에게 다양한 포맷으로 제공할 수 있는 인터페이스가 제공되어야 한다. 무엇보다도 공공정보의 공개와 재사용성을 위한 국가적 차원의 거버넌스와 지원이 마련되어야 공공정보의 Linked Data 플랫폼이 온전히 이루어질 수 있을 것이다.

• Smart Media Journal
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• v.9 no.2
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• pp.99-109
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• 2020

This paper proposes a platform construction plan for the use of spectrum big data, collects and analyzes the big data in the radio wave field, establishes a linkage plan, and presents a support system scheme for linking and using the spectrum and public sector big data. It presented a plan to build a big data platform in connection with the spectrum public sector. In a situation where there is a lack of a support system for systematic analysis and utilization of big data in the field of radio waves, by establishing a platform construction plan for the use of big data by radio-related industries, the preemptive response to realize the 4th Industrial Revolution and the status and state of the domestic radio field. The company intends to contribute to enhancing the convenience of users of the big data platform in the public sector by securing the innovation growth engine of the company and contributing to the fair competition of the radio wave industry and the improvement of service quality. In addition, it intends to contribute to raising the social awareness of the value of spectrum management data utilization and establishing a collaboration system that uses spectrum big data through joint use of the platform.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.257-262
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• 2021

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.18-20
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• 2022

디지털플랫폼정부 구현을 위해 공공데이터 전면개방을 추진하고 있다. 이에 따라 해양기상정보 데이터를 안전하고 신뢰성 있게 활용할 수 있는 체계를 확립하고 서비스 품질 향상을 위해 개선방안 마련이 필요하여 연구를 진행하였다.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.253-262
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• 2018

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.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.459-469
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• 2021

In this paper, a case study of basic data science practice education for field teachers and pre-service teachers was studied. In this paper, for basic data science education, spreadsheet software was used as a data collection and analysis tool. After that, we trained on statistics for data processing, predictive hypothesis, and predictive model verification. In addition, an educational case for collecting and processing thousands of public big data and verifying the population prediction hypothesis and prediction model was proposed. A 34-hour, 17-week curriculum using a spreadsheet tool was presented with the contents of such basic education in data science. As a tool for data collection, processing, and analysis, unlike Python, spreadsheets do not have the burden of learning program- ming languages and data structures, and have the advantage of visually learning theories of processing and anal- ysis of qualitative and quantitative data. As a result of this educational case study, three predictive hypothesis test cases were presented and analyzed. First, quantitative public data were collected to verify the hypothesis of predicting the difference in the mean value for each group of the population. Second, by collecting qualitative public data, the hypothesis of predicting the association within the qualitative data of the population was verified. Third, by collecting quantitative public data, the regression prediction model was verified according to the hypothesis of correlation prediction within the quantitative data of the population. And through the satisfaction analysis of pre-service and field teachers, the effectiveness of this education case in data science education was analyzed.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.633-644
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• 2014

In this research, focusing on the public data access function and the communication function of SNS's, an educational program was implemented for the PC and mobile environments. The program offers functionality to share short messages with friends so that problems can be solved while communicating these messages back and forth. With each visiting page, a method for communication was provided, and each room had a announcements page, Q&A section, discussion forum, and file sharing space to facilitate more communication, and by adding the 'search for friends', and 'recommended friends' functionality, it was possible to study with friends and other unknown people using the program. The applications of the educational program were proposed through the analysis of survey results of elementary and high school students of the program.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.201-207
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• 2021

As artificial intelligence gradually expands into jobs, iIt is necessary to nurture talents with AI literacy capabilities required for non-majors. Therefore, in this study, based on the necessity and current status of AI education, AI literacy competency improvement education was conducted for non-majors so that AI learning could be sustainable in relation to future majors. For non-majors at University D, problem-solving solutions through project-based data analysis and visualization were applied over 15 weeks, and the AI ability improvement and effectiveness of learners before and after education were analyzed and verified. As a result, it was possible to confirm a statistically significant level of positive change in the learners' data analysis and utilization ability, AI literacy ability, and AI self-efficacy. In particular, it not only improved the learners' ability to directly utilize public data to analyze and visualize it, but also improved their self-efficacy to solve problems by linking this with the use of AI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.64-69
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• 2022

Commercialization of public technology is the transfer of government-led scientific and technological innovation and R&D results to the private sector, and is recognized as a key achievement driving economic growth. Therefore, in order to activate technology transfer, various machine learning methods are being studied to identify success factors or to match public technology with high commercialization potential and demanding companies. However, public technology commercialization data is in the form of a table and has a problem that machine learning performance is not high because it is in an imbalanced state with a large difference in success-failure ratio. In this paper, we present a method of utilizing CTGAN to resolve imbalances in public technology data in tabular form. In addition, to verify the effectiveness of the proposed method, a comparative experiment with SMOTE, a statistical approach, was performed using actual public technology commercialization data. In many experimental cases, it was confirmed that CTGAN reliably predicts public technology commercialization success cases.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.37-43
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• 2022

The fundamental basis of AI technology is learningable data. Recently, the types and amounts of data collected and produced by the government or private companies are increasing exponentially, however, verified data that can be used for actual machine learning has not yet led to it. This study discusses the conditions that data actually can be used for machine learning should meet, and identifies factors that degrade data quality through case studies. To this end, two representative cases of developing a prediction model using public big data was selected, and data for actual problem solving was collected from the public data portal. Through this, there is a difference from the results of applying valid data screening criteria and post-processing. The ultimate purpose of this study is to argue the importance of data quality management that must be most fundamentally preceded before the development of machine learning technology, which is the core of artificial intelligence, and accumulating valid data.