• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.807-813
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• 2001

본 논문에서는 기업교육에 있어서 e-Learning을 효과적으로 실현하기 위해 Information Filtering을 제안하고자 한다. 사용자 profile에 기반하여 지식 경영상 시스템을 기업교육에 도입함으로써 정보 검색 시 term space에서 모든 단어를 vector로 나타내어, 사용자 profile과 비교 측정하여 다음 유사한 측정을 통해서 원하는 정보 문서를 사용자에게 제공한다. Information Filtering의 도입으로 사용자의 흥미 변화에 맞춰 다이나믹하게 공급되는 학습 문서속에서 기업을 위한 e-Learning으로 경영성과를 높이는 하나의 전력을 제시한다.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.139-145
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• 2001

As e-commerce has been activated and internet has been used as usual, higher efficient search engine must be used to promote the value of information and take possession of the market place. all e-commerce user seller and buyer want to competitive goods Although these needs, search results are still much to be desired. In this paper, I will suppose two ideas which are abbreviation result and making blacklist. Abbreviation result is to hide results with common factors and making blacklist is to reduce null links of search results, which makes many useless results. This routine is made of making blacklist, check list, reduce list and append list.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.85-96
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• 2023

With advance of cloud computing technology, Blockchain as a Service of Cloud Service Provider has been utilized in various areas such as e-Commerce and financial companies to manage customer history and distribution history. However, if users' search history, purchase history, etc. are to be utilized in a BaaS in areas such as recommendation algorithms and search engine development, the users' search queries will be exposed to the company operating the BaaS, and privacy issues will be occured. Z. Guan et al. ensure the unlinkability between users' search query and search result using searchable encryption, and based on the inner product similarity, they select Top-k results that are highly relevant to the users' search query. However, there is a problem that the Top-k results selection may be not possible due to ties of inner product similarity, and BaaS over cloud is not considered. Therefore, this paper solve the problem of Z. Guan et al. using cosine similarity, so we improve accuracy of search result. And based on this, we design a BaaS with secure Top-k search that improved accuracy. Furthermore, we design a smart contracts that preserve privacy of users' search and obtain Top-k search results that are highly relevant to the users' search.

• Venture DIGEST
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• s.73
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• pp.10-11
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• 2005

‘정보 검색’과‘게임’을 양대 축으로 삼아 인터넷 산업의 척후병 역할을 감당하고 있는 NHN. 젊은 생각과 앞선 기술력을 바탕으로 정상의 자리를 지키고 있는 NHN의 최휘영 대표는 올해부터 국내 사업 부문을 맡아 새로운 도약을 위해 역량을 집중하고 있다. 디지털라이프 실현을 향해 떨리는 가슴으로 한걸음씩 전진하고 있는 최대표에게서 설레는 고백을 들어본다.

• 건강소식
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• v.35 no.3
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• pp.38-39
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• 2011

서울 강남구 역삼동 강남파이낸스센터 22층에 위치한 구글코리아. 세계 최대 검색 기업인만큼 치열하고 분주한 일상이 펼쳐질 것 같은 구글의 일터는 회사라기보다 대학교와 유사한 분위기다. 카페테리아에서 자유롭게 토론을 벌이기도 하고 점심시간이면 당구대와 미니 축구게임기 앞에서 모여 게임을 즐긴다. 직원들의 건강을 위한 마사지 프로그램은 전 세계 구글에서 실시하는 프로그램이다.

• Proceedings of the CALSEC Conference
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• 1999.11a
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• pp.129-142
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• 1999

협의: 컴퓨터 네트워크(인터넷)을 활용한 상거래 비즈니스 광의: 컴퓨터 네트워크를 활용하여 수행되는 광고, 수발주, 설계, 기술개발, 생산, 판매, 결제 등 모든 경제 활동 유형: -구조화된 전자상거래 : EDI나VAN을 활용 특정기업간 규정된 거래조건하에서 상거래 -전자시장에서의 전자상거래: WWW을 활용 불특정 기업간: 최적의 상대를 찾아 거래하는 것 (중략)

• Digital Contents
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• no.9 s.124
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• pp.158-164
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• 2003

기업들은 트랜잭션 정보와 메타데이터 확보하기 위해 해마다 수십억 달러를 지출하고 있다.전자상거래 물결은 각종 트랜잭션을 자동화된 전자 프로세스로 전환시켜 이러한 비용의 상당부분을 절감할 수 있도록 하고 있다. 하지만 대부분의 기업들은 여전히 종이로 문서들을 교환하고 있다. 결과적으로 이들은 데이터 입력, 분류보관, 문서검색, 복사, 팩스, 다른 형태로의 문서 재구성에 아직도 수십억 달러를 지출하고 있다는 것이다.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.416-426
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• 2005

A B2Bi system needs a message storage subsystem that efficiently logs and searches XML messages which have been sent from orreceived by it. XML database systems and XML enabled relational databases systems are not adequate as a message storage system because of their expensiveness and excessiveness in functionality. Storage schemes that split XML messages into database records are also unacceptable because of either low performance or implementation hardness. In this paper, we propose a storage scheme that can be applied to implement a message storage system based on a relational database system. In this scheme, messages are examined only through the index fields that have been registered for each message types. Therefore, the proposed storage scheme cannot support such a powerful search facility like XQL, but it provides high performance message legging and restricted search facility. There are three alternative database schemas to store the index fields. This paper compares the advantages and disadvantages of the three schemas through experimental tests.

• The Magazine for Energy Service Companies
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• s.32
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• pp.22-23
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• 2005

인터넷에서 중학생이라는 단어를 검색하였더니, 많은 내용이 학습과 관련된 것이었고, 사춘기의 갈등과 고민을 겪으면서 성장하는 시기, 그리고 친구와 많이 사귀면서 안목을 넓히는 것이 중요한 시기 등이 특징이었다. 곰곰이 생각하니 ESCO중학생에게도 적용이 된다.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.