• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.971-977
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• 2010

A flood of biological data has caused many challenges in computing. Bioinformatics, the application of computational techniques to analyze the information associated with biomolecules on a large-scale, has now firmly established itself as an interdisciplinary subject in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics, proteomics, systems biology, biostatistics to computer science. In this review, I provide an introduction and overview of the current state of bioinformatics. Looking at the types of biological information and databases that are commonly used, I also deals with some of bioinformatics application domains which are closely related to areas of computer science.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.123-137
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• 2002

생물정보공학을 위한 학문적/실용적 접근은 전산학, 생물학, 유전공학, 수학/통계학등이 유기적으로 통합되어 이루어져야 한다. 그러나 각계의 전문가가 서로의 특정 지식을 활용하기 위한 물리적인 기반이 갖추어져 있지 않은 상태에서는 각 분야의 전문적 지식 활용이 용이하지 않다. 현재의 의료 서비스 제공자/병원이 가진 방대한 의료 데이터를 생물정보공학 령역에서 활용할 수 있도록 해야 하고, 진료 데이터에 근거한 유전적 정보 분석을 위해 생물학 전문가들이 생성하는 인간 질병에 관한 유전적 분석, 연구 결과를 다시 의료 서비스 제공자에게 돌려주는 순환적 사이클이 필요하고, 이러한 순환적 사이클 지원자는 정보 기술이라고 생각한다. 인간 질병 극복과 좀 더 나은 진료, 예방책을 제공할 수 있도록 생물정보공학, 의료정보학, 컴퓨터과학의 통합 활용 목표를 설정할 수 있다. 각계의 전문가가 지식을 공유할 수 있고 기존의 병원 시스템 및 유전 연구소 등의 시스템을 통합하여 유기적으로 엮음으로써 데이터를 의미 있게 해석하고 공유할 수 있도록 지원하는 프레임워크가 절실히 요구된다. 본 세미나에서는 의료정보학과 생물정보공학에서 활용하는 시스템 통합, 전문 지식의 통합적 활용을 위해 각 전문가를 대신하는 에이전트로 구성된 멀티에이전트 플랫폼을 제시하여, 각 분야가 갖는 전문성 확보, 광고, 유기적 연결을 멀티에이전트 시스템에게 위임함으로써 각 영역에서 서비스 할 수 있는 내용과 서비스 제공 주체인 각계의 전문가 집단을 유기적으로 통합하고자 한다. 의료 영역에서 이루어진 의료 영상 통신 시스템 (Picture Archiving and Communication Systems), 의료 정보 표준화를 위한 HL7 (Health Level 7)에 대해서 경북대학교 지능정보 연구실에서 연구, 개발한 내용을 발표한다. 의료 정보 시스템과 생물학 영역의 유전체 정보 데이터베이스 시스템 사이에 의미 있는 데이터 전송, 지식 획득을 위해 정보 기술 분야에서 활용해야 할 영역으로 XML Web Services, Multi-agent Systems, 전문가 컴뮤니티를 위한 그룹웨어 연구 개발에 관해 사례 중심으로 발표한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1175-1182
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• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.501-510
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• 1999

We have seen a rapid development of multimedia intustry as computer evolves and the internet has changed our way of life dramatically in these days. There we several attempts to teach elementary statistics on the web but most of them are based on commercial products. The need for statistical data analysis and decision making based on those analysis is growing. In this article we try to show one way of reaching that goal by using a server side scripting language PHP3 toghether with extra graphical module and statistical distribution module on the web. We showed some elementary exploratory graphical data analysis and statistical inferences. There are plenty of room of improvements to make it a full blown statistical analysis tool on the web in the new future. All the programs and databases used in our article we public programs. The main engine PHP3 is included as an apache web server module so it is very light and fast. It will be much better when the PHP4(ZEND) will be officially out in terms of processing speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.146-147
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• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.