• 산업공학
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• 제18권2호
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• pp.205-220
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• 2005

This study presents a web log analysis model for e-CRM, which combines the on-line customer's purchasing pattern data and transaction data between companies in B2B environment of make-to-order company. With this study, the customer evaluation and the customer subdivision are available. We can forecast the estimate demands with periodical products sales records. Also, the purchasing rate per each product, the purchasing intention rate, and the purchasing rate per companies can be used as the basic data for the strategy for receiving the orders in future. These measures are used to evaluate the business strategy, the quality ability on products, the customer's demands, the benefits of customer and the customer's loyalty. And it is used to evaluate the customer's purchasing patterns, the response analysis, the customer's secession rate, the earning rate, and the customer's needs. With this, we can satisfy various customers' demands, therefore, we can multiply the company's benefits. And we presents case of the 'H' company, which has the make-to-order manufacture environment, in order to verify the effect of the proposal system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.465-468
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• 2002

Today large corporations are constructing distributed server environment. Many corporations are respectively operating Web server, FTP server, Mail server and DB server on heterogeneous operation. However, there is the problem that a manager must manage each server individually. In this paper, we present distributed FTP server for log mining system on ACE. Proposed log mining system is based upon ACE (Adaptive Communication Environment) framework and data mining techniques. This system provides a united operation with distributed FTP server.

• 한국정보처리학회논문지
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• 제6권11호
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• 한국조리학회지
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• 제24권2호
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• pp.34-43
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• 2018

The objective of this study was to acquire a general and text-based awareness and recognition of cruise food hygiene through big data analytics. For the purpose, this study collected data with conducting the keyword "food hygiene, cruise" on the web pages and news on Google, during October 1st, 2015 to October 1st, 2017 (two years). The data collection was processed by SCTM which is a data collecting and processing program and eventually, 899 kb, approximately 20,000 words were collected. For the data analysis, UCINET 6.0 packaged with visualization tool-Netdraw was utilized. As a result of the data analysis, the words such as jobs, news, showed the high frequency while the results of centrality (Freeman's degree centrality and Eigenvector centrality) and proximity indicated the distinct rank with the frequency. Meanwhile, as for the result of CONCOR analysis, 4 segmentations were created as "food hygiene group", "person group", "location related group" and "brand group". The diagnosis of this study for the food hygiene in cruise industry through big data is expected to provide instrumental implications both for academia research and empirical application.

• 정보처리학회논문지D
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• 제18D권2호
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• pp.81-88
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• 2011

시퀀스(sequence) 데이터가 주어졌을 때 그 중에서 빈번(frequent)한 순차 패턴을 찾는 순차 패턴 마이닝(sequential pattern mining)은 여러 어플리케이션(application)에 사용되는 중요한 데이터마이닝 문제이다. 순차 패턴 마이닝은 웹 접속 패턴, 고객 구매 패턴, 특정 질병의 DNA 시퀀스를 찾는 등 광범위한 분야에서 사용된다. 본 논문에서는 맵리듀스(MapReduce) 프레임웍 상에서 맵리듀스 함수 호출을 최적화하는 순차 패턴 마이닝 알고리즘을 개발하였다. 이 알고리즘은 여러 대의 기계에 데이터들을 분산시켜 병렬적으로 빈번한 순차 패턴을 찾는다. 실험적으로 다양한 데이터를 이용하여 파라미터 값을 변화시켜가며 제안된 알고리즘의 성능을 종합적으로 확인하였다. 그리고 실험 결과를 통해 제안된 알고리즘은 기계 수에 대해 선형적인 속도 개선을 보인다는 것을 확인하였다.

• 컴퓨터교육학회논문지
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• 제22권3호
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• pp.67-78
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• 2019

본 연구의 목적은 Web of Science(WoS)에서 제공하는 학술 빅데이터를 활용하여 정보윤리 이슈를 탐색하고 향후 정보과 정보윤리 교육을 위한 시사점을 제공하는 것에 있다. 이를 위해 WoS에서 제공하는 학술논문 중 정보윤리와 관련해 출판된 318편의 논문을 텍스트 마이닝 하였다. 구체적으로는 R을 활용해 주요키워드에 대한 빈도 분석(TF, DF, TF-IDF), 토픽 모델링 기반의 정보윤리 이슈 분석, 그리고 각 이슈에 대한 연도별 출연 빈도를 분석하여 정보윤리 연구의 경향성을 탐색하였다. 주요 결과를 살펴보면 다음과 같다. 첫째, TF-IDF를 통해 'digital', 'student', 'software', 'privacy' 등의 단어가 주요 키워드임을 확인하였다. 둘째, 토픽 모델링 분석 결과, 'Professional value', 'Cyber-bullying', 'AI and Social Impact' 등을 포함한 총 8개 이슈로 분석되었고, 그 중, 'Professional value'와 'Cyber-bullying' 이슈가 상대적으로 높은 비율을 차지하고 있었다. 본 연구는 이러한 분석 결과를 기초로 우리나라 정보윤리 교육을 시사점을 논의하였다.

• 한국산학기술학회논문지
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• 제10권8호
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• pp.1877-1893
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• 2009

폭소노미(folksonomy)는 웹에 존재하는 리소스에 대해 사용자가 자유롭게 선택한 태그(tag)를 붙여서 정보를 체계화하는 새로운 분류 체계이다. 폭소노미 기반의 시스템에서는 사용자들의 협력태깅에 의해 사용자, 태그, 리소스사이의 관계를 나타내는 3항원 소데이터가 생성된다. 이와 같은 폭소노미 데이터는 웹 리소스에 대한 정보체계화를 위한 메타데이터로서 시맨틱 웹과 웹2.0 분야에 활용되고 있다. 본 논문에서는 다종다양한 폭소노미 데이터를 다양한 관점으로 분석하여 유용한 정보를 추출하기 위한 형식개념분석 기반의 폭소노미 데이터 마이닝 기법을 제안하고, 이를 지원하기 위한 분석도구 FMT를 개발하였다. 또한, 제안한 기법과 FMT의 유용성을 검증하기 위하여, 폭소노미 기반 시스템인 del.icio.us의 데이터를 대상으로 실험을 수행하고, 그 결과를 보고한다.

• Journal of Information Technology Applications and Management
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• 제17권2호
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• pp.71-90
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• 2010

Electronic learning(or e-learning) has created hype for companies, universities, and other educational institutions. It has led to the phenomenal growth in the use of web-based learning and experimentation with multimedia, video conferencing, and internet-based technologies. Many researchers are interested in the factors that affect to the performance of e-learning or e-learning services. In this sense, this study is aimed at proposing e-learning system reuse prediction models in which e-learner intention to reuse influence factors(i.e., system accessibility, system stability, information clarity, information validity, self-regulated efficacy, computer self-efficacy, perceived usefulness, perceived ease of use, flow, and parental expectation) affect e-learner intention to reuse positively. A web survey was conducted for the full members of the e-learning education institute A in Seoul, Republic of Korea, an exclusive e-learning company that provides real time video lectures via the desktop conferencing system. The web survey was conducted for 20 days from November 5, 2009, through the e-learning web site of the company A. In this study, three data mining techniques were used : the multivariate discriminant analysis, CART, and C5.0 algorithm. This study was conducted to provide the e-learning service providers, e-learning operators, and contents developers with marketing and management strategies for improving the e-learning service companies, based on the data mining analysis results.

• 디지털산업정보학회논문지
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• 제5권1호
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• pp.9-22
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• 2009

This paper is an explanation of a design and an implementation of Web 2.0 online travel agency system for frequent decision-making. On the Web 2.0 travel agency system, optimized information is obtained by applying data mining technology such as association rules, decision trees, and neural networks, and this system is a unified system that consists of the block systems of hotels, ground traffic, and flights in tour packages of a travel agency system. Furthermore, it is implemented to manage the system that is not for the administrator of a travel agency system, but for users or communities that use the system need their own information. The expected effect of this system is to maximize the investment company's efficiency through a new-concept interest model created by B2C customers, and also B2B small and medium-sized travel agencies adopting the system. As a result, it is a system that stimulates dormant customer activity and prevents good customers from leaving by maximizing the merit and capacity of the existed web site for marketing. Moreover, this system is also a model for people who plan customized travel agency business, and will show a way for the domestic and international travel agency industry's globalization.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2007년도 추계학술대회
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• pp.269-274
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• 2007

Ultimate goal of this research is to develop a web-based forecasting system of industrial accidents. As an initial step for the purpose of this study, this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. In addition, this paper presents the logical process for development of a forecasting system. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. The sample for this work was chosen from 10,536 data related to manufacturing industries during three years(2002$^{\sim}$2004) in korea.