• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.429-432
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• 2017

자동차 보급률 증가로 인해 교통 혼잡, 불법 주정차 등의 사회적 문제가 발생하고 있다. 특히 불법 주정차는 교통 혼잡, 주차 공간 부족 등 부가적인 문제를 발생시키고 있다. 따라서 각 지방자치단체에서는 불법 주정차 문제를 해결하기 위한 방안을 연구하고 있다. 그러나 이러한 방안은 초기 비용 발생 및 인력 부족 등의 한계가 있다. 한편, 정보통신의 발달에 따라 공공 업무에도 대량의 공공데이터를 효율적으로 처리하기 위한 연구가 진행되고 있다. 하지만 이러한 연구 또한 빅데이터 처리 플랫폼 부족 및 분석 시스템이 미흡한 한계가 존재한다. 따라서 본 논문에서는 불법 주정차 데이터와 같은 공공 데이터를 효율적으로 처리하기 위해, 주정차 단속 시스템을 위한 하둡 기반 대용량 데이터 관리 및 분석 시스템을 제안한다. 제안하는 시스템은 첫째, 주차단속을 수행할 때 주차단속 데이터를 하이브(Hive)를 통해 저장하고, 단속된 차량의 차주를 검색하여 단속임을 알리거나 과태료를 부과한다. 둘째, 웹 인터페이스를 통해 수집된 주차단속 데이터에 대한 다양한 분석을 수행하고, 분석된 데이터에 대한 R을 이용한 시각화를 제공한다.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.15-21
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• 2016

Under the flag of creative economy, Korea government is now releasing public data in order to develop or provide a range of services. In this paper, we develop a customized trip navigation system to recommend a trip itinerary based on integration of open government data and personal tourist data. The system uses case-based reasoning (CBR) to provide a personalized trip navigation service. The main difference between existing trip information systems and ours is that our system can offers a user-oriented information service. In addition, our system supports Turn-key style contents provision to maximize convenience. Our system can be a good example of the way in which open government data can be used to design a new service.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.409-410
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• 2019

점진적 빅데이터 학습 기반의 전자저널 구독가치 큐레이션 서비스는 대용량의 학술정보 처리환경을 하드웨어 기반에서 소프트웨어 기반으로 데이터를 학습함에 있어 학습 소요시간 및 메모리 부족 문제 등을 해결하기 위해 널리 사용하는 자질축소 기법에 의존하지 않고 대량의 데이터를 자유롭게 학습하고 증분 데이터 변경요소만을 추가 반영할 수 있는 범용적이고 일반적인 분류기의 구조설계 방법이다. 학술정보의 논문요약과 참고문헌의 데이터 수집 정제 분류 저장 분석을 통해 활용할 수 있는 지표를 생성하여 도서관 학교 공공기관 연구기관 등에 제공하여 기관에서 구독하고 있는 학술지가 연구에 얼마나 활용되고 있는지를 판단하는 정보 가용성을 활용한 양질의 정보원을 확보하여 불필요한 저널 구독을 중단하고 연구자가 요구하는 품질 좋은 학술정보를 제공할 수 있는 서비스로 일반적인 학술문헌 이용도 평가방법과 달리 구독 가치에 대한 지표를 제공하는 큐레이팅 방법이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1248-1251
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• 2013

오늘날 학교는 교육수요자 중심으로 운영되며 정규 교육과정 이외의 교육활동이 많아지고 있다. 이러한 사교육의 증가는 사회적으로 이슈화 되고 있으며 정부나 교육청 같은 공공기관에서 방과후 활동 등 새로운 정책이나 방안을 제시하고 있지만 문제가 해결되지 않고 있다. 이러한 문제를 빅데이터 기술을 활용하여 교육에 앞서 학생들의 관심, 생각, 문제 등을 정보를 추출 및 분석하여 교육의 방향을 제시함으로써 사교육의 감소를 도모하고자 한다. 본 논문에서는 이러한 문제점을 해결하고자 빅데이터를 활용함으로써 교육청에게 학생들의 정보를 통계적으로 제공하여 교육 제도의 올바른 방향으로 정책 할 수 있도록 방안을 제시한다.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.55-59
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• 2018

Big-data, a revolutionary technology in the era of the 4th Industrial Revolution, provides services in various fields such as health, public sector, distribution, marketing, manufacturing, etc. It is very useful technology for marketing analysis and future design through accurate and quick data analysis. It is very likely to develop further. However, the biggest problem when using Big-data is privacy and privacy. When various data are analyzed using Big-data, the tendency of each user can be analyzed, and this information may be sensitive information of an individual and may invade privacy of an individual. Therefore, in this paper, we investigate the necessary measures for Personal private information infringement that may occur when using Personal private information in Big-data environment, and propose necessary Personal private information protection technologies to contribute to protection of Personal private information and privacy.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.181-188
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• 2020

The fisheries industry is rapidly shifting from a traditional fishery to aquaculture paradigm and it faces various problems such as depletion of fishery resources and aging of fishing villages. We need the establishment of a fisheries big data platform that includes both the data of the central and surrounding industries of the fisheries industry for enhancement of establishment of a fisheries, 6th industrialization of fishing villages, establishment of related technical standards, and discovery of the new industries to overcome this. Data center agencies should collect, link, and pre-processing, and the platform organizer should create a water industry data virtuous circle through the establishment, operation, and data market of big data platforms to help overcome the current crisis, secure smart fisheries hegemony, and use it as a key to value transfer. Through this study, I would like to propose a policy and technical big data platform construction plan to successfully promote it.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.217-233
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• 2020

This study evaluated the urban vitality of Daegu metropolitan city in 2018 using emerging geographic data such as spatial big data, Wi-Fi AP(access points) and nighttime light satellite image. The emerging geographic data were used in this research to quantify human activities in the city more directly at various spatial and temporal scales. Three spatial big data such as mobile phone data, credit card data and public transport smart card data were employed to reflect social, economic and mobility aspects of urban vitality while public Wi-Fi AP and nighttime light satellite image were included to consider virtual and physical aspects of the urban vitality. With PCA (Principal Component Analysis), five indicators were integrated and transformed to the urban vitality index at census output area by temporal slots. Results show that five clusters with high urban vitality were identified around downtown Daegu, Daegu bank intersection and Beomeo intersection, Seongseo, Dongdaegu station and Chilgok 3 district. Further, the results unveil that the urban vitality index was varied over the same urban space by temporal slots. This study provides the possibility for the integrated use of spatial big data, Wi-Fi AP and nighttime light satellite image as proxy for measuring urban vitality.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.39-46
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• 2021

When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.41-49
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• 2015

This study explores the possibility of innovative government's administrative services to the public by reflecting the social implications of 'Big Data'. In particular, the idea of OPP (Order Penetration Point) and SOP (Service Offering Point) as a management scheme for the extension of administrative processes into the resident's living space is proposed to overcome the inherent limits of e-government service quality, the root cause of which is believed in the segregation of two spaces - the resident's living space and the government's offices. Furthermore, a discussion is made on how to integrate sensor data from the resident's living space with traditional administrative database, which is a new challenge in the course of synchronizing the two spaces. The implications on the process extension are also provided centering around the shift from reactive to proactive services.

• 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.