• 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.

• Journal of the Korea Fashion and Costume Design Association
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• v.8 no.3
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• pp.1-15
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• 2006

The purpose of this research is to find out the effect of clothing style and mixtures of stripe pattern direction, width, and coloration of an upper garment. The experimental materials made for this study are a set of stimuli and response scale(The 7-Point semantic). The subjects were 480 female undergraduate students in Seoul, Busan, Gyung-nam. The 80 stimuli which were variously incorporated with clothing styles and stipe patterns were assessed by the students. The following contents summarizes the results of the research. Impression factors of the stimuli consists of the 5 dimensions(Attractiveness, Show, Activity, Clearness, mildness). Amon these, the Attractiveness and Show dimensions proved to be more important. The clothing style and pattern direction indicated main effect in attractiveness and activity dimension. The pattern coloration showed a significant effect in dimensions except attractiveness and clearness. The pattern width had a significant effect only in the activity. Significant interaction effects of each clothing clause were found in the attractiveness, activity and clearness, but Show and mildness had no significant interaction effect. This research, as the 06 S/S, F/W trend, is a meaningful study in that it handled in the form of expression the stripe pattern used unrestrictedly in the casual wear or the formal wear by a fashion keyword.

• The Journal of Pediatrics of Korean Medicine
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• v.31 no.1
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• pp.1-11
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• 2017

Objectives The purpose of this study is to investigate commonly-used-pattern-identifications and to understand herbal medicine treatment for psoriasis based on recent clinical studies. Methods Keyword 'psoriasis' was used to search articles in National Digital Science Library (NDSL), Korean Traditional Knowledge Portal (KTKP), Oriental Medicine Advanced Searching Integrated System (OASIS) and Research Information Sharing Service (RISS). 19 relevant articles were reported between 2006 - 2016, and were obtained and reviewed. Results Among the 19 articles, the most commonly-used-pattern-identifications were 'blood heat pattern'. 'Gunsun-bang', 'Bangpungtongsungsan-gami'. Also, based on the search results, 'Yangdokbaekho-tang' were more frequently prescribed than other herbal medicines, and ingredients such as, Glycyrrhizae Radix (甘草), Saposhnikoviae Radix (防風) and Rehmanniae Radix (生地黃) were used repeatedly in those prescriptions. Conclusions This study showed what pattern identifications there are, and what herbal medicines are often used in clinical treatments. Developing new form of herbal medicines are also going to be possible with further research.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.2
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• pp.176-184
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• 2011

Modern treatment of diabetes with hypoglycemic agents have been resulted the Pathological changes. Therefore, the pattern identifications of Korean traditional medicine also have to change. The aim of this study is to learn about the recent change of the pattern identifications on diabetes mellitus. Chinese traditional medicine is more free than us in medical activities because of the combination of Western and Eastern medical treatment and so there has been much published studies. Therefore, 35 papers that were searched from 2003 to 2010 in China by the keyword '糖尿' & '消渴' through the date base system of Kisti & Cnki were selected and analyzed. 35 review papers were composed of 28 observing academically and 7 clinical research studies. The combination of Western and Eastern Medical treatment has been effective than Western Medical treatment alone and the stasis(瘀血) acted as an important etiology on Diabetes mellitus. In the treatment of diabetes the stasis should be considered consistently from the onset.

• 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.

• Korean Institute of Interior Design Journal
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• v.17 no.5
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• pp.157-165
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• 2008

This study confirm the value and the importance of the human sensibility study to add up the new meaning, and to suggest a new value of Korean sensibility study through the understanding of current statusand trend of the sensibility study in the space. The method of the study was to collect date related to the sensibility study and to analyze it focusing on its details. The date was collected from researches published on the website since the establishment of Korean Institute of Interior Design and Architectural Institute of Korea and selected at the keyword search comer. The data was extracted under keywords of research object, research purpose, research method, and analysis method. And then it was quantified with HAYASH lll program and used for analyses according to its pattern and feature. The study shows that nowadays categories representing the current status and trend of the sensibility studies in space consist of the environment, the human, and the space. The contemporary study for sensibility puts the importance on a object and a subject of the study like the environment harmonized with human and space, the humans the subject that essentially uses the spate, and the space for the architecture and the interior that puts human in. Accordingly, the study for human sensibility should develop into the study for the design focused on the intangible relationship such as 'information', 'elements for space design', 'sensibility' beyond the existing tangible categories of environment, human, and space. In addition, in the method ways of study and analysis, those studies for the sensible relationship are required to develop into new types of study applying research methods of various studies beyond the traditional border between human studies, social science, and natural science.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.476-479
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• 2005

The information available on the Internet has become widely used, primarily due to the ability of Web based E-Commerce and M-Commerce Retrieval Engines to find useful information for users. However, present day Commerce Retrieval Engines are far from perfect because they return results based on simple user keyword matches without any regard for the concepts in which the user is interested. In this thesis, we design and evaluate a Recommender system for web context aware based information retrieval using user profiles. Also, we designed personalization framework in ubiquitous environment based both e-commerce and m-commerce and presented the interaction of user profile including User XQuery pattern in semantic web.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.119-133
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• 2011

With the advent of Web 2.0, the use of RESTful web services is expected to overtake that of the traditional SOAP-based web services. Recently, the growing number of RESTful web services available on the web raises the challenging issue of how to locate the desired web services. However, the existing keyword searching method is insufficient for the bad recall and the bad precision. In this paper, we propose a novel building semantic ontology method which employs both the clustering technique based on association rules and the semantic analysis technique based on patterns. From this method, we can generate ontologies automatically, reduce the burden of semantic annotations, and support more efficient web services search. We ran our experiments on the subset of 168 RESTful web services downloaded from the PregrammableWeb site. The experimental results show that our method achieves up to 35% improvement for recall performance, and up to 18% for precision performance compared to the existing keyword searching method.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.71-75
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• 2017

Due to the spread of SNS, web-based consumer-generated data is increasing exponentially. It is important in many fields to accurately extract what is appropriate for the user's interest in a large amount of data. It is especially important for business mangers to establish marketing policies to find the right customers for them in many users. In this paper, we try to obtain important information centering on customers who are interested in each account through Twitter follow - following relationship. Because Twitter's current follower relationships do not reflect the user's interests, we try to figure out the details of interest using keyword extraction methods for tweets of followers. To do this, we select two domestic commercial Twitter accounts and apply the sequential pattern evaluation index to the mining key phrase of the text data collected from the follower.