• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.7-9
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• 2000

데이터 웨어하우스는 기업의 의사 결정 과정을 향상시킬 수 있게 하는 정보기술이다. 대표적인 정의로는 '기업의 의사결정 과정을 지원하기 위한 주제 중심적이고 통합적이며 시간성을 가지는 비휘발성 자료의 집합 '이다.[1] 즉, 기업들이 보유하고 있는 분산된 대량의 데이터를 추출, 변환, 통합하여 요약된 읽기 전용의 데이터베이스로 구축함으로써, 경영분석이나 기업내의 의사 결정 지원 자료로 주로 활용된다. 데이터 웨어하우스의 경우, 일반사용자는 웨어하우스내에 저장된 데이터를 직접 이용하는 경우가 대부분이다. 따라서, 데이터의 구조와 의미에 대한 일반 사용자의 이해가 필요하게 되었다. 즉, 데이터의 추출 및 정제규칙, 데이터의 통합규칙, 요약알고리즘, 데이터 처리스케쥴 등을 알아야만 한다. 메타데이터는 최소한의 데이터 구조, 데이터의 요약에 사용된 알고리즘, 운영 데이터베이스와 데이터 웨어하우스사이의 대응관계와 같은 정보를 포함하여야 한다.[3] 여기서 변환프로세스에 대한 정보를 데이터의 형식에 대한 정보와 일반적인 데이터들과 차별화하여 메타프로세스라 한다.[5] 메타프로세스는 데이터를 변환하여 데이터 웨어하우스에 적재하는 과정에서 생성되는 메타데이터의 일부로써 데이터 웨어하우스에 통합된 자료들이 어떤 변환과정을 거쳐 생성된 자료인지를 알려주는 변환프로세스에 관한 정보를 제공한다. 본 연구에서는 대부분의 데이터 웨어하우스에서 구현되고 있는 메타데이터들은 데이터 항목의 속성정보를 위주로 한 것이며, 변환 프로세스와 관련된 데이터 관리가 미약하다. 따라서, 데이터 웨어하우스의 메타데이터 중 메타프로세스 정보의 추출 및 관리 시스템을 제안하는 것이다.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.343-354
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• 2005

The goal of this paper is to compare classification performances and to find a better classifier based on the characteristics of data. The compared methods are CART with two ensemble algorithms, bagging or boosting and SVM. In the empirical study of twenty-eight data sets, we found that SVM has smaller error rate than the other methods in most of data sets. When comparing bagging, boosting and SVM based on the characteristics of data, SVM algorithm is suitable to the data with small numbers of observation and no missing values. On the other hand, boosting algorithm is suitable to the data with number of observation and bagging algorithm is suitable to the data with missing values.

• Proceedings of the KAIS Fall Conference
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• 2007.11a
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• pp.155-158
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• 2007

현대사회의 발전으로 인해 생성되는 수많은 정보는 시간과 공간의 제약이 없는 다차원적인 특성을 갖고 있으며, 사회 전반에 걸쳐 보다 나은 결과를 위한 의사결정에 활용되고 있다. 또한 우리생활에서 발생하는 많은 현상을 보다 합리적이고 과학적인 방법으로 분석하여 정확한 예측이 이루어진다면 미래의 불확실성에 대한 불안을 해소하고, 현재의 의사 결정을 하는데 큰 도움이 될 수 있다. 따라서 본 논문에서는 다층 신경망을 이용하여 비선형적 관계를 표현할 수 있는 적응 능력을 갖을 뿐만 아니라 비선형 통계예측에 적용이 가능한 알고리즘을 제안하고 분석하였다.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.51-61
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• 2016

With high interest in the patient satisfaction of emergency medical services, there is a lot of effort into improving the process of Emergency Department(ED) utilizing the technology of Internet of Things(IoT). In this study, the core technologies of smart ED are examined and a decision support algorithm for medicine tasks is proposed. The proposed algorithm minimizes the decision risks such as task selection accountability, patient complaints, care delays and longer stay time. It can reduce the nurses burnout and improve the patient care with kindness and consideration. Ultimately, patient satisfaction, job satisfaction and professional identity of nurses can be increased. The comparative study was carried out by simulation in terms of the average length of patient stay in a simplified hypothetical ED system. In all the cases, the proposed algorithm was shown to perform substantially better than the other rule.

• Journal of Korean Society of Steel Construction
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• v.19 no.1
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• pp.29-41
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• 2007

Structural design, like other complex decision problems, involves many trade-offs among competing criteria. Although mathematical programming models are becoming increasingly realistic, they often have design limitations, that is, there are often relevant issues that cannot be easily captured. From the understanding of these limitations, a decision-support system is developed that can generate some useful alternatives as well as a single optimum value in the optimization of steel frame structures. The alternatives produced using this system are "good" with respect to modeled objectives, and yet are "different," and are often better, with respect to interesting objectives not present in the model. In this study, we created a decision-support system for designing the most cost-effective moment-resisting steel frame structures for resisting lateral loads without compromising overall stability. The proposed approach considers the cost of steel products and the cost of connections within the design process. This system makes use of an optimization formulation, which was modified to generate alternatives of optimum value, which is the result of the trade-off between the number of moment connections and total cost. This trade-off was achieved by reducing the number of moment connections and rearranging them, using the combination of analysis based on the LRFD code and optimization scheme based on genetic algorithms. To evaluate the usefulness of this system, the alternatives were examined with respect to various design aspects.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.517-523
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• 2004

The container load planning is one of key factors for efficient operations of handling equipments at container ports. When the number of containers are large, finding a good solution using the conventional genetic algorithm is very time consuming. To obtain a good solution with considerably small effort, in this paper a pseudo-parallel genetic algorithm(PPGA) based on both the migration model and the ring topology is developed The performance of the PPGA is demonstrated through a test problem of determining the optimal loading sequence of the containers.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.877-884
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• 2011

The decision tree is a representative algorithm of data mining and used in many domains such as retail target marketing, fraud detection, data reduction, variable screening, category merging, etc. This method is most useful in classification problems, and to make predictions for a target group after dividing it into several small groups. When we create a model of decision tree with a large number of input variables, we suffer difficulties in exploration and analysis of the model because of complex trees. And we can often find some association exist between input variables by external variables despite of no intrinsic association. In this paper, we study on the removal method of unnecessary input variables using multiple external association rules. And then we apply the removal method to actual data for its efficiencies.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.671-678
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• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.6
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• pp.824-834
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• 1993

In cellular mobile communication systems, if the size of a cell is decreasing for economic utilization of frequency resources, frequent handovers may be requested because the time a mobile stays in a cell is decreasing. In general the measured parameters to decide handover including RSSI, BER, and the distance between mobile station and base station, are usually incorrect and handover decision using single parameter insufficient. Therefore, the better handover algorithm should take over the problems of this uncertain measurements, and make the decision more robust and flexible by the consideration of all those decision parameters at the same time. We propose a novel handover algorithm based the multicriteria decision making, in which those parameters are participated in the decision process using aggregation function in fuzzy set theory. As a simulation results, the overall decision making is more reliable and flexible than the conventional method using only one parameter, RSSI in terms of call force ratio, and handover request ratio.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.57-61
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• 1998

FCM은 비구조적인 (unstructured) 문제영역에서 주어진 문제에 대한 효과적인 추론시 적용될 수 있는 매우 유용한 추론도구이다. 그러나, FCM에 사이클이 존재하면 추론효과가 크게 감소한다. 본 논문에서는 사이클이 있는 FCM을 이용하여 의사결정의 질을 높일 수 있는 추론방법을 제시한다. 이를 위하여 FCM내에 존재하는 사이클을 확인하고 해소하는 알고리즘을 제시한다. 아울러 사이클이 제거된 FCM의 추론의 질을 저하시키는 문제중의 하나인 동기화 문제 (levelization) 알고리즘을 제시한다.