• 지식경영연구
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• 제5권1호
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• pp.69-82
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• 2004

Prior research on knowledge management focused more on the experiential knowledge based on individual's experience or knowhow than on the analytical knowledge extracted from corporate data. This study examines the effects of the data warehouse technology, especially OLAP(on line analytical processing) and data mining techniques, on the analytical knowledge creation in organizations, linking analytical knowledge creation to data analysis method through real world case studies.

• 지식경영연구
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• 제19권1호
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• pp.119-132
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• 2018

Many advanced products and services are emerging in the market thanks to data-based technologies such as Internet (IoT), Big Data, and AI. The construction of a system for data processing under the IoT network environment is not simple in configuration, and has a lot of restrictions due to a high cost for constructing a high performance server environment. Therefore, in this paper, we will design a development environment for large data analysis computing platform using open source with low cost and practicality. Therefore, this study intends to implement a big data processing system using Raspberry Pi, an ultra-small PC environment, and open source API. This big data processing system includes building a portable server system, building a web server for web mining, developing Python IDE classes for crawling, and developing R Libraries for NLP and visualization. Through this research, we will develop a web environment that can control real-time data collection and analysis of web media in a mobile environment and present it as a curriculum for non-IT specialists.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• 대한산업공학회지
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• 제32권3호
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• pp.242-252
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• 2006

Data mining is the exploration and analysis of huge amounts of data to discover meaningful patterns. One of the most important data mining problems is association rule mining. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm is proposed that is based on a link structure between transactions. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.191-197
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• 2005

Application of data mining techniques to the world wide web, referred to as web mining, has been the focus of several recent researches. With the explosive growth of information sources available on the world wide web, it has become increasingly necessary to track and analyze their usage patterns. In this study, we introduce a process of pre-processing and cluster analysis on web log data and suggest a distance measure considering the structural relationships between web contents. Also, we illustrate some real examples of cluster analysis for web log data and look into practical application of web usage mining for eCRM.

• 한국빅데이터학회지
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• 제3권1호
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• pp.47-54
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• 2018

비즈니스 프로세스 분석의 기존 연구들은 비즈니스 프로세스에 포함된 업무, 고객 서비스, 작업자 편의, 수행시간 예측 등 다양한 요소를 분석하였다. 이러한 요소를 정확히 분석하기 위해서는 정보시스템에 기록된 실제 이력 데이터를 활용하는 것이 효과적이다. 프로세스 마이닝은 이벤트 로그 데이터로부터 비즈니스 프로세스의 여러 가지 요소를 분석하는 기법이다. 본 사례 연구는 구매 대행 업체의 업무 수행 데이터에 프로세스 마이닝를 적용하여 구매 대행 프로세스의 업무 흐름, 수행 시간, 담당자 등의 프로세스 운영 분석을 수행하였다.

• 한국빅데이터학회지
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• 제1권2호
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• pp.65-78
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• 2016

기업 환경의 급격한 변화와 복잡성의 증가에 따라 다양한 장점을 지닌 기업들이 협력을 통해 고객에게 짧은 시간에 최상의 가치를 제공해주는 것이 중요해 지고 있다. 이를 위해 기업은 다양한 공급망에 참여하게 되고, 기업의 공급망 관리에 대한 중요성은 점차 증대되고 있다. 이러한 공급망 관리의 효율성을 높이기 위해 공급망 상에서 생성되는 데이터의 효과적인 분석이 필요하다. 본 연구에서는 프로세스 마이닝 기법을 활용한 공급망 데이터 분석을 제안한다. 프로세스 마이닝 기법이 적용 가능한 공급망 데이터의 분석 범주를 도출하고, 프로세스 마이닝을 활용한 다양한 분석을 제안하다. 이를 통해 기업은 공급망 관리에 대한 인사이트를 얻고 공급망 관리 프로세스의 개선 및 효율화가 가능하다. 사례 연구를 통해 프로세스 마이닝을 활용한 공급망 데이터 분석의 유효성을 검증하였다.

• Asia pacific journal of information systems
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• 제8권3호
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• 한국안전학회지
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• 제28권1호
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• Journal of the Korean Data and Information Science Society
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• 제23권5호
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• pp.983-992
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• 2012

사회지표조사는 주민들이 생각하는 사회 상태를 총체적으로 파악할 수 있는 조사로서 다양한 시책 개발에 있어 지역의 여론을 반영할 수 있는 장점이 있다. 사회지표조사는 사회 변화를 알 수 있는 중요한 척도라고 할 수 있으며, 많은 지자체 (서울시, 인천시, 부산시, 울산시, 경상남도 등)에서 많은 예산과 시간을 들여 조사를 실시하고 있다. 그러나 조사에 대한 분석 결과가 기초통계분석 위주로 되어 있어 실제 사회지표조사 자료를 제대로 활용하고 있지 못하고 있는 실정이므로 데이터마이닝 등의 다양한 방법의 적용이 필요하다. 이에 본 논문에서는 사회지표조사의 효율적인 분석을 위하여 새로운 데이터마이닝 방법론을 제시하고자 한다. 본 논문에서는 매개연관성규칙, k-평균 군집분석, 의사결정나무를 순차적으로 적용하는 3단계 복합 데이터마이닝의 적용 방법을 제안하며, 이를 2010년에 조사된 경상남도 사회지표조사 자료에 적용하고자 한다.