• The Journal of Information Systems
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• v.31 no.2
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• pp.19-43
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• 2022

Purpose Due to the COVID-19 pandemic, many companies are building virtual workplaces based on virtual reality technology. Through this study, we intend to identify the trend of convergence and convergence research between virtual reality technology and work space, and suggest future promising fields based on this. Design/methodology/approach For this purpose, 12,250 bibliographic data of research papers related to Virtual Reality (VR) and Workplace were collected from Scopus from 1982 to 2021. The bibliographic data of the collected papers were analyzed using Text Mining and Pathfinder Network, Parallel Neighbor Clustering, Nearest Neighbor Centrality, and Triangle Betweenness Centrality. Through this, the relationship between keywords by period was identified, and network analysis and visualization work were performed for virtual reality-based workplace research. Findings Through this study, it is expected that the main keyword knowledge structure flow of virtual reality-based workplace convergence research can be identified, and the relationship between keywords can be identified to provide a major measure for designing directions in subsequent studies.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.1051-1056
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• 2007

In current web-based systems, it is generally recognized that one way flow of information from providers to users can cause the static problem of document structure. Therefore, information update frequency and interaction between providers and users are quiet slow. Monopolized information can obstruct the free user's access and heterogeneous format and different protocols also make users difficult to retrieve and to collect information. To resolve these problems, in this study, we introduce the Web 2.0 to move toward the user's participation and share based on the social network and the OAI protocol to improve the free access and the interoperability on bibliographic information for Life Science and then design the bibliographic information network for life science. This network has four main functions such as: 1) Open Repository function that can make up user community for sharing and data exchange. Data such as article, seminar material, research note and research report are considered in design. 2) Open Collection function that can collect and store the metadata on distributed bibliographic information networks, 3) Open Access function that can manage the metadata in the open access environment, and 4) Administration function that can monitor the user activity and statistics and can inspect the registered data.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.241-264
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• 2022

Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.

• Journal of the Korean Society for information Management
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• v.30 no.4
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• pp.31-59
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• 2013

The purpose of this study was to examine the intellectual structure of domestic LIS in the 1990s and 2000s using author bibliographic coupling analysis (ABCA). First, cluster analysis and multi-dimensional scaling analysis were performed to examine core subject areas and to map authors in two-dimensional space. Second, network analysis was used to visualize intellectual relationships among subject areas and to reveal the top subject areas for global centrality. Third, the 1990s and 2000s intellectual structures was compared to identify the changes of the intellectual structure over the course of time.

• Journal of Information Management
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• v.42 no.4
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• pp.75-94
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• 2011

The purpose of this study is to analyze the domain on the field of Electrical Engineering in Korea by the author bibliographic coupling analysis. The data set contains a total of 2,157 articles from two core journals with 23,411 citation data from 2005 to 2009 published in two prestigious journals. In order to achieve the purpose of this study, MDS analysis, clustering analysis and network analysis were used to examine core subject areas. In addition, the centrality analysis in the weighted networks was used to explore the key authors in this field such as the top global centrality authors and the top local centrality authors. The findings of this study can be utilized to guide the current research trend and author network for collection development and information services in the field of Electrical Engineering.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.18-29
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• 2022

The purpose of this exploratory study is to examine the intellectual structure of data science. For this purpose, this research examined a total of 17,997 bibliographies on data science indexed in Web of Science(WoS) of Clarivate Analytics from 2012 to 2021. This research applied methods such as descriptive analysis, citation analysis, co-author network analysis, co-occurrence network analysis, bibliographic coupling analysis, and co-citation analysis. This research contributes to finding the research directions of future data science topics.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.133-154
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• 2016

Interdisciplinary approach has been recognized as one of key strategies to address various and complex research problems in modern science. The purpose of this study is to investigate the interdisciplinary characteristics and structure of the field of big data. Among the 1,083 journals related to the field of big data, multiple Subject Categories (SC) from the Web of Science were assigned to 420 journals (38.8%) and 239 journals (22.1%) were assigned with the SCs from different fields. These results show that the field of big data indicates the characteristics of interdisciplinarity. In addition, through bibliographic coupling network analysis of top 56 journals, 10 clusters in the network were recognized. Among the 10 clusters, 7 clusters were from computer science field focusing on technical aspects such as storing, processing and analyzing the data. The results of cluster analysis also identified multiple research works of analyzing and utilizing big data in various fields such as science & technology, engineering, communication, law, geography, bio-engineering and etc. Finally, with measuring three types of centrality (betweenness centrality, nearest centrality, triangle betweenness centrality) of journals, computer science journals appeared to have strong impact and subjective relations to other fields in the network.

• Journal of the Korean Society for information Management
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• v.37 no.1
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• pp.153-177
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• 2020

In the context of open science, data sharing and reuse are becoming important researchers' activities. Among the discussions about data sharing and reuse, data journals and data papers shows visible results. Data journals are published in many academic fields, and the number of papers is increasing. Unlike the data itself, data papers contain activities that cite and receive citations, thus creating their own intellectual structures. This study analyzed 14 data journals indexed by Web of Science, 6,086 data papers and 84,908 cited references to examine the intellectual structure of data journals and data papers in academic community. Along with the author's details, the co-citation analysis and bibliographic coupling analysis were visualized in network to identify the detailed subject areas. The results of the analysis show that the frequent authors, affiliated institutions, and countries are different from that of traditional journal papers. These results can be interpreted as mainly because the authors who can easily produce data publish data papers. In both co-citation and bibliographic analysis, analytical tools, databases, and genome composition were the main subtopic areas. The co-citation analysis resulted in nine clusters, with specific subject areas being water quality and climate. The bibliographic analysis consisted of a total of 27 components, and detailed subject areas such as ocean and atmosphere were identified in addition to water quality and climate. Notably, the subject areas of the social sciences have also emerged.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.169-177
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• 2023

Network security is now more crucial than ever for consumers, companies, and military clients. Security has elevated to the top of the priority list since the Internet's creation. The evolution of security technology is now better understood. The area of community protection as a whole is broad and dynamic. News from the days before the internet and more recent advancements in community protection are both included in the topic of observation. Recognize current research techniques, previous Defence strategies that were significant, and network attack techniques that have been used before. The security of various domain names is the subject of this article's description of bibliographic research.