• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.94-98
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• 2010

첨단과학의 발달과 정보화 시대는 전쟁수행 패러다임을 근본적으로 변화시키고 있다. 이러한 전장 환경의 변화와 더불어 군 내부에서도 정보기술의 급격한 변화를 적극적으로 수용하고 21세기 국방을 건설하기 위한 국방 정보화 목표를 앞당기기 위해 수많은 정보기술체계를 도입하고 적용하고 있다. 이러한 수많은 체계들의 유입 속에서 각 체계간의 상관관계를 규명하여 효과적으로 사용하는 것은 중요한 문제로 대두되고 있으며, 이러한 문제의 해결방법으로 최근에 대두되고 있는 방법론이 사회연결망 분석(SNA : Social Network Analysis)이다. 사회연결만 분석은 무수히 많은 정보의 홍수 속에서 그들의 관계를 파악하고, 임무수행에 필요한 중요 노드를 식별하여 불필요한 자원을 낭비 하지 않은 중요한 요소를 분석 할 수 있는 유용한 방법론으로 사회, 문화 전반에 걸쳐 활발히 연구되고 있다. 본 논문에서는 이러한 사회연결망분석을 통해 지휘통제체계와 연계된 노드간의 관계를 규명하고, 관계망속에 속한 정보의 획득 및 흐름을 파악하여 이를 시각화하고자 한다. 이를 통해 현재 운용중인 지휘통제체계 구조에 대해 이해하기 쉽고, 용이하게 파악 할 수 있으며, 이를 기반으로 지휘구조의 조직과 조직적인 행동과 현상을 도출하고, 향후 군 네트워크 파워를 측정 할 수 있다.

• Journal of Contemporary Eastern Asia
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• v.12 no.1
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• pp.65-79
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• 2013

This paper examines communication patterns of key Twitter users by considering the socially and politically controversial Sejong City issue in South Korea. The network and message data were drawn from twtkr.com. Social network-based indicators and visualization methods were used to analyze political discourse among key Twitter users over time and illustrate various types of Tweets by these users and the interconnection between these key users. In addition, the study examines general Twitter users' participation in the discussion on the issue. The results indicate that some Twitter profiles of media outlets tend to be very dominant in terms of their message output, whereas their Tweets are not likely to be circulated by other users. Noteworthy is that Twitter profiles of individuals who are geographically affiliated with the issue are likely to play an important role in the flow of communication.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.3
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• pp.491-511
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• 2016

This paper attempts to understand the nature and dynamics of global value chains in the auto industry using the OECD TiVA 2015 edition on the bilateral foreign value added in exports over the period 1995-2011 and employing the techniques of social network analysis such as the computation of network measures and visualization of value added trade flows. It is shown that there has been a tendency towards increasing production fragmentation both within and across regions. The automotive value-added network is found to have small-world properties with a hierarchical, clustered and dense structure. The differences among the US, Germany and China as major suppliers of foreign value added in global automotive value chains are remarkably revealed. Although the fragmentation of production has been developed on a global scale, a dichotomous tension between center and periphery and domestic and foreign capital lies behind it.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1431-1438
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• 2014

We analyzed the articles from "Kukje Shinmun" and "Busan Ilbo", which are two local newpapers of Busan Metropolitan City. The articles cover from January 1, 2013 to December 31, 2013. Meaningful pattern inherent in 2889 articles of which the title includes "Busan" and "Traffic" and related data was analyzed. Textmining method, which is a part of datamining, was used for the social network analysis (SNA). HDFS and MapReduce (from Hadoop ecosystem), which is open-source framework based on JAVA, were used with Linux environment (Uubntu-12.04LTS) for the construction of unstructured data and the storage, process and the analysis of big data. We implemented new algorithm that shows better visualization compared with the default one from R package, by providing the color and thickness based on the weight from each node and line connecting the nodes.

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.330-347
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• 2023

Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.727-736
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• 2014

The fundamental concerns of this paper are to analyze the effects of some characteristics on the employment of new college graduated students in viewpoint of data visualization. We use individual and department characteristic data of K-university graduated students in 2010. We apply multiple correspondence analysis, decision tree analysis, association rules and social network analysis for data visualization. The results of the analysis are summarized as follows. First, an analysis of the determinants of employment shows that GPA, department category, age and number of majors, recruiting time affect the employment rate. Second, higher GPA and natural category of department positively affect the employment rate. Finally, low age, single major and early recruiting time also positively affect the employment rate.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.393-402
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• 2015

Human resources are one of the most essential elements of an organization. In particular, the more closed a group is, the higher the value each member has. Previous studies have focused on personal attributes of individual, such as medical history, and have depended upon self-diagnosis to manage structures. However, this method has weak points, such as the timeconsuming process required, the potential for concealment, and non-disclosure of participants' mental states, as this method depends on self-diagnosis through extensive questionnaires or interviews, which is solved in an interactive way. It also suffers from another problem in that relations among people are difficult to express. In this paper, we propose a multi-faced diagnosis model based on social network analysis which overcomes former weaknesses. Our approach has the following steps : First, we reveal the states of those in a social network through 9 questions. Next, we diagnose the social network to find out specific individuals such as victims or leaders using the proposed algorithm. Experimental results demonstrated our model achieved 0.62 precision rate and identified specific people who are not revealed by the existing methods.

• Smart Media Journal
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• v.10 no.1
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• pp.47-54
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• 2021

Web accessibility is presented as a legal obligation to ensure users can access and use information and functions equally in the domestic public website sector. This study introduces the researcher network based on social network analysis (SNA) in order to show the citation relationship by identifying the network among researches in the field of web accessibility. Based on the analysis of the relationship and citation of academic researchers in the domestic web accessibility field, confirm the process of creating a research network. It is a visualization element that shows the citation relationship as well as the relationship and citation form between the studies as a network. Through the process of creating the network, and it can be confirmed and can be usefully used for future academic research network analysis. It is meaningful in terms of analysis for about 17 years from January 2000 to March 2017, a total of 50 papers published in journals registered by the National Research Foundation of Korea and candidate journals. By combining the evaluation metrics for each researcher, evaluation target, evaluation method, and evaluation result for web accessibility, and applying weights, identify the research direction and evaluation research trends in Korea's web accessibility field related to the relevant field and web accessibility evaluation research trends, and related to the researcher trend network.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.309-314
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• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.