• Journal of the Korean Society for Library and Information Science
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• v.51 no.1
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• pp.271-287
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• 2017

This study aims to conduct a big data analysis of public library operations and uses of 16 metropolitan local government of Korea by using the Chernoff face method. This study is the first to use the Chernoff face method for big data analysis of library services in library and information research. The association of variables and human facial features was decided by survey. The study reveals that in general the provincial governments in Korea operate more libraries, invest more budgets, allocate more staff and hold more collections than metropolitan cities. This administration resulted in more use of libraries in provincial governments than metropolitan cities.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.49-55
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• 2019

This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.279-306
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• 2023

This study analyzed bibliographic attributes related to the selection and use of reading materials based on data on books borrowed or purchased together in order to understand the properties of reading materials that have complex user needs from various perspectives. As a result of creating co-occurrence matrices of bibliographic attribute terms by dividing them into 26 sub-attribute units related to KDC main class, target reader, and user age, and performing network analyses, the details and prominent mediating role of bibliographic attributes of reading materials were identified. The results of this study will be helpful in designing facets of reading information systems, including library OPAC, in the future.

• Journal of Korean Library and Information Science Society
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• v.39 no.2
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• pp.355-373
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• 2008

The purpose of this study is to find out user's information needs in public libraries based on websites. The data were collected from the user's plaza in public libraries websites. To identify the user's information needs, the works in the public libraries including acquisition, information services, circulation, digital library, reading room, reference services were analyzed.

• The Korean Journal of Financial Management
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• v.17 no.2
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• pp.277-292
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• 2000

본 연구는 Charnes, Cooper와 Rhodes에 의하여 고안된 DEA기법을 이용하여 서울지역에 소재하고 있는 지역신용협동조합의 운영효율성을 평가하였다. 여기에서는 금융기관은 금융서비스를 제공하는 생산체로 보아 예수금은 산출물로 취급하여, 투입물은 직원수 영업장의 면적(또는 업무용부동산), 경비 등으로 하고, 산출물은 신규예금취급건수, 신규대출건수, 예수금기말잔액, 대출금기말잔액, 영업이익 등으로 하여 DEA모형의 데이터로 사용하였다. DEA에 의한 연구 결과는 기존의 경영평가방법이 제공할 수 없는 유용한 정보를 제공하고 있으며, 이러한 정보를 이용하여 투입물과 산출물을 효율적으로 관리할 수 있다는 것을 보여 준다. 그러므로 DEA에 의한 분석정보와 기존의 경영평가방법에 의한 정보를 적절하게 사용하면 조합들의 운영효율성을 개선하는 데 도움이 될 것이다.

• Information and Communications Magazine
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• v.33 no.2
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• pp.52-58
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• 2016

핀테크는 금융과 기술을 결합한 용어로 글로벌 ICT 기업이 폭넓은 사용자 기반을 바탕으로 송금, 결제, 대출, 자산관리 등 각종 금융서비스를 결합하여 제공하는 새로운 유형의 금융서비스를 말한다. 핀테크의 등장은 스마트폰 이용의 보편화로 소비자의 소비행태가 모바일 중심으로 변화하고 있고, 빅데이터 분석 등으로 소비자에게 맞춤형 금융서비스가 가능해진 환경에 기인한다. 핀테크는 전자상거래와 금융서비스가 새롭게 만나면서 자연스럽게 나타나는 현상이다. 핀테크는 기술을 핵심 요소로 하는 금융서비스 혁신으로 파괴적 혁신이라는 특징을 지닌다. 본고에서는 서론에서 핀테크의 정의, 발전 배경을 살펴보고, 본론에서 시장동향과 주요 기업의 사례 분석과 핀테크에 의한 금융 혁신 및 금융회사의 대응 동향을 살펴보았으며, 나아가 핀테크 성공요인 및 주요국의 핀테크 정책을 살펴 보았다. 결론에서는 우리나라의 현황 분석 및 대응 방향을 제시하였다. 정부는 올해 들어 창조경제의 일환으로 '핀테크 육성'을 금융 개혁의 핵심이슈로 선정하고 개혁을 추진 중에 있다. 정부는 핀테크 창업을 통해서 청년문제 등 일자리 문제를 해소하고, 중위험/중금리 사업모델인 인터넷전문은행의 선정, 각종 규제의 개선 등으로 우리경제가 저성장의 늪에서 벗어나 재도약하는 디딤돌이 되길 기대하고 있다.

• Korea Real Estate Review
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• v.28 no.1
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• pp.65-77
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• 2018

This paper analyzes the redemption risk of renters by estimating the LTV and CoLTV with finance market big data (individual credit information) and housing market big data (actual housing transaction data). The analysis showed that when using LTV, the redemption risk was higher in the case of the monthly renter than of the chonsei renter. On the other hand, when using CoLTV, the chonsei renter had a higher redemption risk than the monthly renter. This implies that there is a need to activate a guarantee system, such as risk management using the CoLTV index and the chonsei deposit return guarantee because it is possible for renters to experience losses on their chonsei deposits due to the higher redemption risk. Another implication is that the risk manager should consider the individual characteristics of renters because of the different effects of the redemption risk stemming from the characteristics of the rental contract and the personal characteristics of the renters. CoLTV was just a concept until this study calculated it using housing big data and actual housing transaction information. It helps identify the redemption risk through the characteristics of renters and their contracts.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.333-340
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• 2023

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a post-explanation of the bankruptcy prediction process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2038-2045
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• 2010

Households' monthly average income is composed of various factors. This study paper studies focuses on estimating the determinants of a households' monthly average income. The region for analysis consist of three groups, that is, the whole country, a metropolitan city(such as Busan, Daegu, Incheon, Gwangiu, Daejeon, Ulsan.) and Seoul. Analyzing period be formed over a 57 time points(2005. 01~2009. 09). In this paper the dependent variable setting up the households' monthly average income, explanatory (independent) variables are composed of the consumer price index, employment to population ratio, Index of housing sale price, the preceding composite index, loans of housing mortgage, spending rate for care medical expense and the composite stock price index. In looking at the factors which determine the monthly average income, evidence was produced supporting the hypothesis that there is a significant positive relationship between the composite index and housing loans. The study also produced evidence supporting the view that there is a significant negative relationship between employment ratios, the house sale pricing index and spending rates for care or medical needs. The study found that the consumer price index and composite stock price index were not significant variables. The implications of these findings are discussed for further research.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.225-231
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• 2010

Transaction value of business-to-business(B2B) is composed of various factors for groups and time series. In this paper, we use the panel data for finding various variables and using this we analyse the factors that is major influence to transaction value of business-to-business. For analysis we looked at transaction value of business-to-business of 7 groups such as manufacturing industry, electric, gas and piped water industry, construction industry, retail & wholesale trade, traffic industry, publish, image; broad-casting & telecommunication and information service industry, etc. In our analysis we looked at the transaction value of business-to-business during the period from 2005.01 to 2009.12. We examined the data in relation to the transaction value of cyber shopping mall, company bond, composite stock price index, transaction value of credit card, loaned rate of interest in deposit bank, rate of exchange looking at the factors which determine the transaction value of business-to-business, evidence was produced supporting the hypothesis that there is a significant positive relationship between the transaction value of cyber shopping mall, composite stock price index and loaned rate of interest in deposit bank, rate of exchange. The company bond is negative relationship, transaction value of credit card is positive relationship and they are not significant variables in terms of the transaction value of business-to-business.