• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.57-77
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• 2014

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.101-126
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• 2017

Data Science is emerging as a closely related field of study to Library and Information Science (LIS), and as an interdisciplinary subject combining LIS, statistics and computer science in an attempt to understand the value of data by applying what LIS has been doing for collecting, storing, organizing, analyzing, and utilizing information. To investigate which subject fields other than LIS, statistics, and computer science are related to Data Science, this study retrieved 667 materials from Web of Science Core Collection, extracted terms representing Web of Science Categories, examined subject fields that are studying Data Science using descriptive analysis, analyzed the intellectual structure of the field by co-word analysis and network analysis, and visualized the results as a Pathfinder network with clustering created with the PNNC clustering algorithm. The result of this study might help to understand the intellectual structure of the Data Science field, and may be helpful to give an idea for developing relatively new curriculum.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.1
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• pp.345-372
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• 2007

In this study, the intellectual structure of Records Management & Archival Science in Korea was analyzed using document clustering, a widely used method of text mining, and document similarity network analysis. The data used in this study were 145 articles written on the subject of Records Management & Archival Science selected from five major representative journals in the field of Library & Information Science in Korea, published from 2001 to 2006. The results of cluster analysis show that the core subject areas are "electronic records management and digital Preservation," "records management policy and institution," "records description and catalogues." and "records management domain and education." The results of document analysis, which is more detailed than cluster analysis, show that "digital archiving," a specialized subject in digital preservation, plays a central role. The results of serial analysis, which proceeds according to a timeline, show the emergence of "archival services" as a new subject area.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.86-91
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• 2016

KARI(Korea Aerospace Research Institute) has been developing the KPLO(Korea Pathfinder Lunar Orbiter) for Korean first lunar exploration, and analysing various subjects for the mission success. Especially the performance of the communication is one of important factors, because massive scientific and technical data acquired by multiple payloads might be transferred to ground stations on the Earth. In this paper, we explained the study on the 1-day average downlink capacity based on the visibility analysis between ground stations and KPLO, and described its results.

• Journal of Information Management
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• v.43 no.1
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• pp.159-185
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• 2012

The purposes of this study were to identify core journals for pharmaceutical researches in Korea and to examine the research domains of Korean pharmaceutical researchers. Journal citation frequency analysis and journal co-citation analysis were performed on the research papers of Korean pharmaceutical researchers. Korean researchers' citation data were gathered from SCOPUS for foreign journals and KSCD for domestic journals. 116 core journals were identified through citation frequency analysis and journal relationships were suggested as a pathfinder network of journals. Factor analysis on journal correlation matrix resulted in 18 subject domains and related journal lists were also given.

• Korean Journal of Social Welfare
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• v.68 no.2
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• pp.185-211
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• 2016

This study analyzes key word networks of the papers which are published at Korean Journal of Social Welfare issued by Korean Academy of Social Welfare from 1979 to 2015. It aims at investigating the trends of social welfare researches in Korea by dividing the given period into two: 1979-2000 and 2001-2015. It shows the trends in three ways: methodologies, subjects, and intellectual structures. In order to identify intellectual structure, it calculate centrality indices basing on co-appearance frequency of key words. It also derives some values which explain relationship structure of key words by using pathfinder algorithm, and finally visualizes the intellectual structures by using the NodeXL program. Some implications of the findings of these analyses are discussed in the end.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.59-68
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• 2011

As the role of soccer game analyst who analyzes soccer games and creates soccer wining strategies is emphasized, it is required to have high-level analysis beyond the procedural ones such as main event detection in the context of IT based broadcasting soccer game research community. In this paper, we propose a novel approach to generate the high-level in-depth analysis results via real-time text based soccer Webcast and text mining. Proposed method creates a metadata such as attribute, action and event, build index, and then generate available knowledges via text mining techniques such as association rule mining, event growth index, and pathfinder network analysis using Webcast and domain knowledges. We carried out a feasibility experiment on the proposed technique with the Webcast text about Spain team's 2010 World Cup games.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.2
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• pp.27-49
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• 2015

The purpose of this study is to suggest an appropriate index for evaluating preferences of Web contents by examining the h-index and its variants. It focuses on how successfully each index represents relative user preference towards topical subjects. Based on data obtained from a popular IT blog (engadget.com), subject values of the h-index and its variants were calculated using 53 subject categories, article counts and the 'Likes' counts aggregated in each category. These values were compared through critical analysis of the indices and Spearman rank correlation analysis. A PFNet (Pathfinder Network) of subjects weighted by $h_T$ values was drawn and cluster analysis was conducted. Based on the four criteria suggested for the evaluation of Web contents, we concluded that the $h_T$-index is a relatively appropriate tool for the Web contents preference evaluation. The $h_T$-index was applied to visually represent the relative weight (topic preference by user 'Likes' count) for each subject category of the real online contents after suggesting the relative appropriateness of the $h_T$-index. Applying scientometric indicators to Web information could provide new insights into, and potential methods for, Web contents evaluation. In addition, information on the focus of users' attention would help online informants to plan more effective content strategies. The study tries to expand the application area of the h-type indices to non-academic online environments. The research procedure enables examination of the appropriateness of the index and highlights considerations for applying the indicators to Web contents.

• Journal of KIISE
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• v.43 no.1
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• pp.1-12
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• 2016

As the need for a flexible Internet grows, research for software and virtual routers has increased. Although software routers and virtual routers provide Internet flexibility, they have low performance compared with existing hardware routers. In addition, the low performance problem is intensified in virtual routers because they have virtualization overheads. GPU routing is one method of improving the performance of software routers. However, previous GPU routing is based on native software routers, which are not virtualized, and presents experimental simulation results only. In this paper, we examine the effect of GPU routing on a virtual router using PathSavanna. Our GPU routing is implemented on the virtual router and forwards real packets to another machine, which is connected by a network.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.147-178
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• 2021

In this study, co-word analysis was conducted to investigate the intellectual structure of the domestic open access area. Through KCI and RISS, 124 research articles related to open access in Korea were selected for analysis, and a total of 1,157 keywords were extracted from the title and abstract. Network analysis was performed on the selected keywords. As a result, 3 domains and 20 clusters were extracted, and intellectual relations among keywords from open access area were visualized through PFnet. The centrality analysis of weighted networks was used to identify the core keywords in this area. Finally, 5 clusters from cluster analysis were displayed on a multidimensional scaling map, and the intellectual structure was proposed based on the correlation between keywords. The results of this study can visually identify and can be used as basic data for predicting the future direction of open access research in Korea.