• Journal of Korean Library and Information Science Society
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• v.51 no.2
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• pp.273-294
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• 2020

The purpose of this study is to provide an in-depth analysis on the publication patterns of the 1,029 journal articles by the Life Sciences' faculty at the K University, for the years 2004-2017 and to investigate any difference between their publication patterns and the citedness for the years 2004-2018. Among the three funding agency types, the research publications supported by foreign funding received the more citations than publications by other types of funding, and the study also found a statistical difference in the citedness (F = 10.467, p < .000***). In addition, the internationally co-authored publications received more citations than three other types of co-authored publications in terms of the immediacy index per publication and the average citations per publication.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.79-86
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• 2019

It is very important to analyze and provide information resources for research output produced in the computer field, the core science of the 4th Industrial Revolution. In this paper, SCI journals cited from domestic journals in the computer field were identified and the citation rankings and their co-citation networks were generated, analyzed, mapped and visualized. For this, the bibliographic and citation index information from 2015 to 2017 in the KSCD were used as the basis data, and the co-citation method and network centrality analysis were used. As a result of this study, the number of citations and the citation ranks of SCI journals and papers cited by korean journals in the computer field were analyzed, and peak time(2 years), half-life(6.6 years), and immediacy citation rate(2.4%) were measured by citation age analysis. As a result of network centrality analysis, Three network centralities(degree, betweenness, closeness) of the cited SCI journals were calculated, and the ranking of journals by each network centrality was measured, and the relationship between the subject classifications of the cited SCI journals was visualized through the mapping of the network.

• Journal of the Korean Society for information Management
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• v.28 no.2
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• pp.117-133
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• 2011

There have been little comprehensive research for studying impact of foreign journals on Korean scientists. The main reason for this is because there was no extensive citation index database of domestic journals for analysis. Korea Institute of Science and Technology Information (KISTI) built the Korea Science Citation Database (KSCD), and have provided Korea Science Citation Index (KSCI) and Korea Journal Citation Reports (KJCR) services. In this article, citing behavior of Korean scientists on foreign journals was examined by using KSCD that covers Korean core journals. This research covers (1) analysis of foreign document types cited, (2) analysis of citation counts of foreign journals by subject and the ratio of citing different disciplines, (3) analysis of language and country of foreign documents cited, (4) analysis of publishers of journals and whether or not journals are listed on global citation index services and (5) analysis for current situation of subscribing to foreign electronic journals in Korea. The results of this research would be useful for establishing strategies for licensing foreign electronic journals and for information services. From this research, immediacy citation rate (average 1.46%), peak-time (average 3.9 years) and half-life (average 8 years) of cited foreign journals were identified. It was also found that Korean scientistis tend to cite journals covered in SCI(E) or SCOPUS, and 90% of cited foreign journals have been licensed by institutions in Korea.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.