• 한국디지털건축인테리어학회논문집
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• 제10권2호
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• pp.13-20
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• 2010

Uncertainty in housing price fluctuation has great impact on the overall economy due to importance of housing market as both place of residence and investment target. Therefore, estimating housing market condition is a highly important task in terms of setting national policy. Primary indicator of the housing market is a ratio between rent and transaction price of housing. The research explores dynamic relationships between Rent-Transaction price ratio, housing transaction price and jeonse rental price, using Vector Autoregressive Model, in order to demonstrate significance of shifting rent-transaction price that is subject to changes in housing transaction and housing rental market. The research applied housing transaction price index and housing rental price index as an indicator to measure transaction and rental price of housing. The price index and data for price ratio was derived from statistical data of the Kookmin Bank. The time-series data contains monthly data ranging between January 1999 and November 2009; the data was log transformed to convert to level variable. The analysis result suggests that the rising ratio between rent-transaction price of housing should be interpreted as a precursor for rise of housing transaction price, rather than judging as a mere indicator of a current trend.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제5권6호
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• pp.738-747
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• 1999

본 논문에서는 분산 실시간 시스템의 특징인 분산 처리 과정의 신뢰성을 지원하기 위한 동적 트랜잭션 처리 구조를 연구하였다. 실시간 분산 처리 환경에서 동적으로 발생하는 실시간 분산 트랜잭션 처리를 위하여 트랜잭션 내에 필수적인 3가지 언어적 특성들을 제시하였다. 첫째는 트랜잭션 내에 실시간 시스템의 가장 중요한 특징인 시간적인 제약 조건들을 정의 할 수 있는 방안을 제시하고, 둘째는 비동기적인 처리 성격을 지닌 실시간 특성을 고려한 비동기적 트랜잭션 처리 방법을 제시한다. 또한, 분산 처리 과정에서 발생되는 예외 사항들을 처리하기 위하여 긴급성을 고려한 다중레벨 우선순위 스케줄링 (Multi-Level Priotiry Scheduling)이라 부르는 트랜잭션 스케줄링 방안을 제시한다. 그리고, 제시한 실시간 분산 트랜잭션 처리 구조의 타당성 및 가능성을 입증하기 위한 실시간 트랜잭션 처리 과정을 시물레이션을 통하여 제시한 언어적 특성에 대한 고려 사항들을 보여준다.Abstract We propose a dynamic transaction processing model that supports a reliability for distributed real-time processing. For the dynamic processing in distributed real-time transaction systems, we suggest three features that are defined in programming language. First, we propose a specification model to explicitly define the time constraints, needs in real-time distributed processing. Second, we describe an asynchronous transaction processing mechanism based on the real-time characteristics. So, we suggest three communication primitives to support asynchronous transaction processing. Lastly, a scheduling policy based on urgent transaction is suggested to manage the exception occurred during the distributed processing. This scheduling policy is called multi-level priotiry scheduling (MPLS). Based on three features and scheduling policy, we describe a direction to manage a dynamic transaction processing in distributed real-time systems.

• 한국정보통신학회논문지
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• 제18권11호
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• pp.2703-2708
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• 2014

전형적인 버스 시스템 구조는 공용버스 내에 여러 개의 마스터와 슬레이브, 아비터 그리고 디코더 등으로 구성되어 있다. 복수의 마스터가 동시간대에 버스를 이용할 수 없으므로, 아비터는 이를 중재하는 역할을 수행한다. 아비터가 어떠한 중재방식을 선택하는가에 따라 버스 사용의 효율성이 결정된다. 기존의 중재 방식에는 Fixed Priority 방식, Round-Robin 방식, TDMA 방식, Lottery 방식 등이 연구되고 있는데, 버스 전송량, 대기사이클 그리고 우선순위가 주로 고려되어 있다. 본 논문에서는 대기사이클을 고려하는 버스중재 방식을 제안하고, TLM(Transaction Level Model)을 통해 다른 중재 방식과 비교하여 버스 전송량과 대기 사이클을 검증하였다.

• Asia pacific journal of information systems
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• 제23권2호
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• pp.1-20
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• 2013

Social commerce has been gaining explosive popularity, with typical examples of the model such as Groupon and Level Up. Both local business owners and consumers can benefit from this new e-commerce model. Local business owners have a chance to access potential customers and promote their products in a way that could not have otherwise been easily possible, and consumers can enjoy discounted offerings. However, questions have been increasingly raised about the value and future of the social commerce model. A recent survey shows that about a third of 324 business owners who ran a daily-deal promotion in Groupon went behind. Furthermore, more than half of the surveyed merchants did not express enthusiasm about running the promotion again. The same goes for the case in Korea, where more than half of the surveyed clients reported no significant change or even decrease in profits compared to before the use of social commerce model. Why do local business owners fail to exploit the benefits from the promotions and advertisements through the social commerce model and to make profits? Without answering this question, the model would fall under suspicion and even its sustainability might be challenged. This study aims to look into problems in the current social commerce transactions and provide implications for the social commerce model, so that the model would get a foothold for next growth. Drawing on justice theory, this study develops theoretical arguments for the effects of transaction characteristics on consumers' distributive justice and purchase intention in the social commerce. Specifically, this study focuses on two characteristics of social commerce transactions-the discount rate and the purchase rate of products-and investigates their effects on consumers' perception of distributive justice for discounted transactions in the social commerce and their perception of distributive justice for regular-priced transactions. This study also examines the relationship between distributive justice and purchase intention. We conducted an online experiment and gathered data from 115 participants to test the hypotheses. Each participant was randomly assigned to one of nine manipulated scenarios of social commerce transactions, which were generated based on the combination of three levels of purchase rate (high, medium, and low) and three levels of discount rate (high, medium, and low). We conducted MANOVA and post-hoc ANOVA to test hypotheses about the relationships between the transaction characteristics (purchase rate and discount rate) and distributive justice for each of the discounted transaction and the regular-priced transaction. We also employed a PLS analysis to test relations between distributive justice and purchase intentions. Analysis results show that a higher discount rate increases distributive justice for the discounted transaction but decreases distributive justice for the regular-priced transaction. This, coupled with the result that distributive justice for each type of transaction has a positive effect on the corresponding purchase intention, implies that a large discount in the social commerce may be helpful for attracting consumers, but harmful to the business after the promotion. However, further examination reveals curvilinear effects of the discount rate on both types of distributive justice. Specifically, we find distributive justice for the discounted transaction increases concavely as the discount rate increases while distributive justice for the regular-priced transaction decreases concavely with the dscount rate. This implies that there exists an appropriate discount rate which could promote the discounted transaction while not hurting future business of regular-priced transactions. Next, the purchase rate is found to be a critical factor that facilitates the regular-priced transaction. It has a convexly positive influence on distributive justice for the transaction. Therefore, an increase of the rate beyond some threshold would lead to a substantial level of distributive justice for the regular-priced transaction, threrby boosting future transactions. This implies that social commerce firms and sellers should employ various non-price stimuli to promote the purchase rate. Finally, we find no significant relationship between the purchase rate and distributive justice for the discounted transaction. Based on the above results, we provide several implications with future research directions.

• 농촌계획
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• 제28권2호
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• pp.41-50
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• 2022

The primary purpose of this study is to identify various factors that affect farmland prices according to changes in the actual transaction price of farmland over the past decade, and to use this to derive policy implications for price stabilization. To this end, the farmland price model are constructed at the parcel level in the case area (Namwon-si, Jinju-si). The analysis method is based on the Hedonic price function, and the OLS and the quantile regression are used for the parcel level model. As a result of estimating the parcel level farmland price model in the case area, the larger the parcel area, the lower the farmland price, and the higher the farmland price outside the agricultural promotion area. It was found that there was a price difference according to the type of special purpose areas, and the location characteristics showed some differences across the cities. The farmland price models presented in this study are suitable for identifying the factors affecting farmland prices, and are expected to be highly utilized in that it is possible to construct flexible variables suitable for regional characteristics.

• 한국시스템다이내믹스연구
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• 제15권4호
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• pp.85-100
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• 2014

We propose a composite make or buy decision model considering both the transaction cost theory and the resource based view in the Korean defense industry using System Dynamics. We analyze relationship between core variables(transaction frequency, technological uncertainty, the level of technological dependency, technological level and acquisition ability for market information) and 'Make or Buy decision' focused on technological innovation capability. Based on the result, we propose the implications as follows : First, the defence industry needs more R&D investment. Second, it needs a balance between domestic(Make) and overseas(Buy) to increase the technological capability rapidly.

• 한국전자거래학회지
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• 제23권1호
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• pp.1-22
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• 2018

가치사슬은 경쟁우위 강화를 위한 전략적 도구로써 주로 기업수준, 산업수준에서 분석되어 왔다. 그런데 기업수준에서 가치사슬 분석을 수행하기 위해서는 분석 기업의 거래처 기업들이 그 기업의 가치 사슬에 속하는지의 여부에 따라 분류되어야 한다. 단일 기업에 대한 가치사슬 분류는 전문가들에 의해 원활히 수행될 수 있지만 다수의 기업을 대상으로 분류할 때는 많은 비용과 시간이 소요되는 등의 한계점이 따른다. 따라서 본 연구에서는 실거래 데이터를 기반으로 특정 기업의 거래처 기업들을 분류해서 가치사슬 기업을 자동적으로 도출해주는 모형을 제안하고자 한다. 총 19개의 거래 속성 변수를 실거래 데이터로부터 도출하여 기계학습의 입력 데이터의 형태로 가공하였고, 랜덤포레스트 알고리즘을 이용하여 가치사슬 분류 모형을 구축하였다. 자동차 부품 기업 사례에 본 연구 모형을 적용한 결과, 정확도 92%, F1-척도 76% 그리고 AUC 94%로 자동적 가치사슬 분류의 가능성을 확인하였다. 또한 거래집중도, 거래금액 그리고 거래처별 총 매출액 등과 같은 거래 속성들이 가치사슬에 속하는 기업들을 대표하는 주요 특성임을 확인하였다.

• 정보보호학회논문지
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• 제26권6호
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• pp.1513-1526
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• 2016

금융회사가 전자금융 서비스를 제공하기 시작하면서 전자금융 서비스는 다양화 되었고 전자금융 사용은 지속적으로 증가하고 있다. 이에 금융회사는 안전한 전자금융서비스를 제공하기 위하여 금융 보안정책을 적용하고 있으나 전자금융 사고는 계속해서 지능화되고 증가하고 있는 상황이다. 금융감독기관은 최근 인터넷 전문은행 등장과 핀테크 활성화와 더불어 비대면 실명확인 제도 신설 및 전자금융 거래를 통한 자금이체 시 공인인증서 또는 일회용비밀번호 의무사용 폐지 등의 규정을 개선하여 이용자의 편리함을 추구하는 동시에 금융회사에게는 이상금융거래 탐지 시스템 고도화 및 개선을 통한 불법이체 사고 방지를 권고하고 있다. 본 논문에서는 금융회사 제반 상황에 적합한 블랙리스트기반 자동화 탐지 기법을 제안하고 블랙리스트 정보를 레벨링하여 보안레벨에 따른 블랙리스트기반과 통계모델을 연동한 실시간 이상금융거래 탐지 기법을 제안하며, 기존 전자금융 사고유형 분석을 통한 특징적 패턴에 따른 실시간 이상금융거래 탐지기법의 대응 모델을 제안하고자 한다.

• 한국IT서비스학회지
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• 제18권2호
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• 산업공학
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• 제18권3호
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• pp.268-276
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• 2005

In recent years, the money Laundering crimes are increasing by means of foreign exchange transactions. Our study proposes four scoring models to provide early warning of the laundering in foreign exchange transactions for both inward and outward remittances: logistic regression model, decision tree, neural network, and ensemble model which combines the three models. In terms of accuracy of test data, decision tree model is selected for the inward remittance and an ensemble model for the outward remittance. From our study results, the accumulated number of transaction turns out to be the most important predictor variable. The proposed scoring models deal with the transaction level and is expected to help the bank teller to detect the laundering related transactions in the early stage.