• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.313-325
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• 2022

The purpose of this study is to examine the changes in the elderly healthcare app market through text mining analysis and to present basic data for activating elderly healthcare apps. Data collection was conducted on Naver, Daum, blog web, and cafe. As for the research method, text mining, TF-IDF(Term frequency-inverse document frequency), emotional analysis, and semantic network analysis were conducted using Textom and Ucinet6, which are big data analysis programs. As a result of this study, a total of six categories were finally derived: resolving the healthcare app information gap, convergence healthcare technology, diffusion media, elderly healthcare app industry, social background, and content. In conclusion, in order for elderly healthcare apps to be accepted and utilized by the elderly, they must have a good diffusion infrastructure, and the effectiveness of healthcare apps must be maximized through the active introduction of convergence technology and content development that can be easily used by the elderly.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.264-266
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• 2012

인터넷 상에서의 변형 단어들을 처리하는 문제는 정보 검색, 기계 번역, 웹 마이닝, 욕설 및 스팸 필터링과 같은 다양한 분야에서 사용될 수 있다. 특히 단어의 변형 추이를 파악하는 등 데이터 수집 및 분석을 위해서는 주어진 단어가 어떤 변형 단어의 집합으로 이루어진 부류에 포함되는지 여부를 파악해야 할 필요성이 있다. 본 논문에서는 같은 부류에 속한 변형 단어 집합에 대하여 다중 서열 정렬(multiple sequence alignment)을 수행함으로써 해당 집합을 하나의 대표 문자열로 취급하는 변환 기법을 제안하고, 이를 이용해 주어진 단어가 해당 부류에 속하는지 여부를 효과적으로 분류하는 기법을 소개한다. 실험결과 제안 기법의 분류 성능은 민감도 93.4% 수준에서 89.1%의 특이도를 보여 전수 비교를 통한 분류에 비하여 결코 성능은 하락하지 않으면서 분류 속도는 16.5배 향상되었음을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.169-171
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• 2005

그래프를 사용하는 데이터 표현법은 직$\cdot$간접적으로 실세계를 표현하는 다양한 데이터 모델 중에서 가장 일반화된 방법으로 알려져 있다. 기본적으로 그래프는 정점과 간선으로 구성되며, 정점과 간선은 그 중요도나 운영 목적에 따라 다양한 가중치가 부여될 수 있다. 특히, 이러한 그래프를 순회하는 트랜잭션들로부터 중요한 순회패턴을 탐사하는 것은 흥미로운 일이다. 본 논문에서는, 정점과 간선에 가중치가 있고 방향성을 가진 기반 그래프가 주어졌을 때, 그 그래프를 순회하는 트랜잭션들로부터 가중치를 고려하여 빈발 순회패턴을 탐사하는 방법을 제안한다. 또한, 이렇게 탐사한 결과에 가중치를 고려한 중요도를 평가하여 빈발 순회패턴들 간의 우선순위를 결정할 수 있도록 한다. 이 과정에서 발생할 수 있는 트랜잭션 노이즈는 기반 그래프의 간선 가중치의 평균과 표준편차를 이용하여 제거함으로써 보다 신뢰성 있는 빈발 순회패턴을 탐사할 수 있다. 제안한 논문은 웹 로그 마이닝 등 그래프를 이용하는 다양한 응용 분야에 적용할 수 있을 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.147-148
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• 2017

빅데이터 분석은 데이터베이스에 잘 정리된 정형 데이터뿐 아니라 인터넷, 소셜 네트워크 서비스, 모바일 환경에서 생성되는 웹 문서, 이메일, 소셜 데이터 등 비정형 데이터를 효과적으로 분석하는 기술을 말한다. 메타분석은 여러 실증연구의 정량적인 결과를 통합과 분석을 통해 전체 결과를 조망할 수 있는 기회를 제공하는 통계적 통합 방법이다. 전자상거래 연구에서 혁신확산에 영향을 미치는 요인으로 상대적 이점, 적합성, 복잡성, 시험 가능성, 관찰 가능성, 편리성 그리고 커뮤니케이션 채널을 외부 요인으로 설정된 연구를 대상으로 하고자 한다. 다음으로 국내 주요 학회지에 게재된 혁신확산이론 관련연구에서 어떠한 요인들을 사용하고 있고 또한 이러한 외부요인들이 종속변수에 어느 정도의 설명력을 가지는지를 메타분석을 통해 알아보고자 한다. 이러한 연구모델을 바탕으로 학문적 실무적 의의를 논의하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.721-722
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• 2009

인터넷 시장이 빠르게 성장함에 따라 사용자들의 참여도가 매우 높아졌다. 인터넷 사용자들은 시장의 상품과 더불어 영화와 같은 문화 생활에 관한 의견을 웹 상에 표현하기 시작했고, 현재에 들어 그 양이 방대해 졌다. 본 논문에서는 사용들이 작성한 영화에 관한 리뷰를 모아 방대한 양에서 유용한 정보를 효율적으로 도출하고 요약해서 사용자에게 제공하는 방법을 제안한다.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.539-548
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• 2007

Mobile content adaptation is a technology of effectively representing the contents originally built for the desktop PC on wireless mobile devices. Previous approaches for Web content adaptation are mostly device-dependent. Also, the content transformation to suit to a smaller device is done manually. Furthermore, the same contents are provided to different users regardless of their individual preferences. As a result, the user has difficulty in selecting relevant information from a heavy volume of contents since the context information related to the content is not provided. To resolve these problems, this paper proposes an enhanced method of Web content adaptation for mobile devices. In our system, the process of Web content adaptation consists of 4 stages including block filtering, block title extraction, block content summarization, and personalization through learning. Learning is initiated when the user selects the full content menu from the content summary page. As a result of learning, personalization is realized by showing the information for the relevant block at the top of the content list. A series of experiments are performed to evaluate the content adaptation for a number of Web sites including online newspapers. The results of evaluation are satisfactory, both in block filtering accuracy and in user satisfaction by personalization.

• Journal of the Korea Society of Computer and Information
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• v.7 no.3
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• pp.149-157
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• 2002

Todays, electronic commerce(EC) results to the revolution and new paradigm of business, more and more Web-based EC applications have emerged. But, it's web systems should be satisfied by customers and it should be successful to buying some goods in virtual stores with easy to use. The usability and acceptance of the EC web system is one of the key factors in the successful construction of EC system. In this paper, we considered the characteristics of information search and decision making process in the design of EC web system to be used easily and to be more acceptable to customers. On the basis of these characteristics, we could classified with the activities of the process of buying in the domestic web systems. And, the log files of experimental tasks were analyzed by the statistical method of data mining. As the these results, the important factors of the process of buying could be summarized, 5 user groups could be seen in EC customers, and the usage patterns of these groups were described. These results could be very useful to design user-oriented EC web system.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.92-103
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• 2003

Great many advertisements are serviced in on-line by development of electronic commerce and internet user's rapid increase recently. However, this advertisement service is stopping in one-side service of relevant advertisement rather than doing users' inclination analysis to basis. Therefore, want advertisement service that many websites are personalized for efficient service of relevant advertisement and service through relevant server's log analysis research and enforce. Take advantage of log data of local system that this treatise is not analysis of server log data and analyze user's Preference degree and inclination. Also, try to propose advertisement system personalized by making relevant site tributary category and give weight of relevant tributary. User's preference user preference which analysis is one part of cooperation fielder ring of web personalized techniques use information in visit site tributary and suppose internet user's action in visit number of times of relevant site and try inclination analysis of mixing form. Express user's preference degree by vector, and inclination analysis result uninterrupted data that simplicity application form is not regarded and techniques that propose inclination analysis change of data since with move data use and analyze newly and proposed so that can do continuous renewal and application as feedback Sikkim. Presented method that can choose advertisements of relevant tributary through this result and provide personalized advertisement service by applying process such as user inclination analysis in advertisement chosen.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.1-11
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• 2016

Recently numerous studies are conducted to estimate the human personality from the online social activities. This paper develops a comprehensive model for political attitude estimation leveraging the Facebook Like information of the users. We designed a Facebook Crawler that efficiently collects data overcoming the difficulties in crawling Ajax enabled Facebook pages. We show that the category level selection can reduce the data analysis complexity utilizing the sparsity of the huge like-attitude matrix. In the Korean Facebook users' context, only 28 criteria (3% of the total) can estimate the political polarity of the user with high accuracy (AUC of 0.82).