• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.1581-1584
• /
• 2015

최근 소셜 네트워크 서비스상의 데이터를 실시간으로 분석하여 의미있는 정보를 찾아내기 위한 연구가 활발하게 진행되고 있다. 특히, 스마트폰과 같은 스마트 디바이스를 이용하는 많은 사용자들이 실시간으로 발생하는 이벤트를 소셜 네트워크상에 게재하고 서로 공유하면서, 대중들이 관심을 가지는 토픽의 경우 굉장히 빠르게 확산되는 경향을 보이고 있다. 본 논문에서는 이러한 SNS의 특성을 토대로 트위터상의 트윗을 분석하여 여러 분야의 토픽들을 카테고리별로 분류하고, 카테고리별 트렌드를 추출하여 실시간으로 시각화하는 기법을 제안한다. 이를 위해, 트위터를 기반으로 SVM 분류 알고리즘과 Twitter-LDA를 통하여 트윗을 분야별로 분류하고, 각각의 트렌드를 이루는 대표적인 키워드를 선출하여 이를 기반으로 실시간 트렌드를 추출한다. 제안하는 기법의 성능을 평가하기 위해, 분류 특징 선택의 신뢰도를 측정한다.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.10
• /
• pp.23-37
• /
• 2013

In this paper, the proposed movie recommender system constructs trust network, which is similar to social network, using user's trust information that users explicitly present. Recommendation on items is performed by using relation degree between users and information of recommended item is provided by a visualization method. We discover the hidden relationships via the constructed trust network. To provide visualized recommendation information, we employ Freebase which is large knowledge base supporting information such as movie, music, and people in structured format. We provide three visualization methods as the followings: i) visualization based on movie posters with the number of movies that user required. ii) visualization on extra information such as director, actor and genre and so on when user selected a movie from recommendation list. iii) visualization based on movie posters that is recommended by neighbors who a user selects from trust network. The proposed system considers user's social relations and provides visualization which can reflect user's requirements. Using the visualization methods, user can reach right decision making on items. Furthermore, the proposed system reflects the user's opinion through recommendation visualization methods and can provide rich information to users through LOD(Linked Open Data) Cloud such as Freebase, LinkedMDB and Wikipedia and so on.

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06a
• /
• pp.13-16
• /
• 2011

최근 들어 네트워크1로 연결된 체계들을 효율적으로 운영하여 최대의 효과를 달성하기 많은 연구들이 수행되고 있다. 하지만 지휘통제체계 네트워크 구조 분석에 관한 연구는 상대적으로 미흡한 실정이다. 따라서 본 연구에서는 지휘통제체계 중 육군의 SPIDER체계를 대상으로 소설 네트워크 분석 (Social Network analysis)기법을 이용하여 중앙성분석과 시각화(Visualization)를 통해 핵심노드를 식별하는 arms value 알고리즘을 제안하고 분석 결과를 기반으로 TICN체계 전력화시 기초 연구자료로 활용하고자 한다.

• Proceedings of the Korea Contents Association Conference
• /
• 2013.05a
• /
• pp.277-278
• /
• 2013

최근 사회적으로 이슈가 되고 있는 소셜 네트워크 서비스 활용하는 시각 장애인의 수가 점차 증가하고 있으나, 시각 장애인들에 대한 배려 및 접근성은 낙제 수준에 머물고 있다. 이는 보편적인 활용성의 측면보다는 일반인만을 대상으로 제작된 것으로 시각장애인이 원활하게 이용하기에 어려움이 존재한다. 본 논문에서는 시각장애인의 SNS 활용을 지원하기 위한 SNS 관리 시스템을 설계하고 구현한다. 제안하는 시스템은 현재 가장 많은 활용도를 보이는 세 개의 SNS의 공통 특성 분석을 통한 통합 포스팅 관리 및 포스팅 공유 기능을 제공하여 개별 관리 도구 개발에서 발생하는 개발 비용을 감소시키는 것이 가능하다. 또한, 수집 데이터를 시각 장애인의 특성을 고려한 인터페이스로 제공함으로써 시각 장애인의 활용성을 극대화 하였다. 뿐만 아니라, 제안하는 시스템은 독립적인 프로그램의 형태로 제공되기 때문에, 기존의 시각 장애인이 보유하고 있는 보조 기기에 탑재하여 활용하는 것이 가능하다.

• The Journal of the Korea Contents Association
• /
• v.16 no.9
• /
• pp.127-135
• /
• 2016

We identified a competition network which has never been studied before and investigated the relationship between centrality of participants in singing competition and their winning percentage within the competition network. We collected competition data from 'Immortal Songs: Singing the Legend', which is a Korean television music competition program, and constructed a competition network. We calculated centrality and winning percentage and analyzed their relationship using correlation analysis, regression analysis, and visualization. There are four main findings in this research. First, a competition network is a scale-free network whose degree distribution follows a power law. Second, there is a logarithmic relationship between the count of competition and closeness. Third, winning percentage converges to approximately 60% for players who have participated in more than 20 competitions. Lastly, a strength of opponents affects approximately 23% of winning percentage for players with less than 20 competitions. The academic significance of this study is that we pioneered the definition of the competition network and applied social network analysis method. Another significant contribution of this paper is that we found explicit patterns between the centrality and winning percentage, suggesting ways to improve social relationship in competition network and to increase winning percentage.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.9
• /
• pp.1199-1205
• /
• 2021

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.7
• /
• pp.149-154
• /
• 2014

Modern data computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. The visualization has proven effective for not only presenting essential information in vast amounts of data but also driving complex analyses. Big-data analytics and discovery present new research opportunities to the computer graphics and visualization community. In this paper, we discuss the patent analysis of big data visualization technology development in major countries. Especially, we analyzed 160 patent applications and registered patents in four countries on November 2012. According to the result of analysis provided by this paper, the text clustering analysis and 2D visualization are important and urgent development is needed to be oriented. In particular, due to the increase of use of smart devices and social networks in domestic, the development of three-dimensional visualization for Big Data can be seen very urgent.

• International Commerce and Information Review
• /
• v.19 no.1
• /
• pp.23-42
• /
• 2017

International Commerce and Information Review has been playing an important role of disseminating the outstanding research results in the fields such as trade information and systems, e-trade, regional studies, e-commerce, service trade, trade laws since 1999. This paper aims to find the research trends and distinguished characteristics in the field of trade information by analyzing research keywords of the research papers published in this journal using a social network analysis method. Research keyword data collected from the homepage of the academic society were cleaned and transformed into the co-occurrence network data, which are suitable for social network analysis. NodeXL Pro was used to analyze and visualize the pre-processed data. Through clustering analysis, the most important subject fields or interests were identified as well as those which worked as intermediaries for interdisciplinary researches.

• Journal of Appropriate Technology
• /
• v.7 no.1
• /
• pp.85-92
• /
• 2021

The research topics of the appropriate technology include environment, medical, education, and energy etc. Therefore, having a review on appropriate technology requires huge efforts of specialists from many difference research areas. Social network analysis has been applied to understand the relationships among the components in the network and to provide insights by visualizing the huge network having many components. In this study, the co-authors and their research topics at the international conference on appropriate technology (ICAT) held by Academic Society for Appropriate Technology (ASAT) from 2017 to 2019 were analyzed using the statistical and visualization tools. The networks between co-authors were analyzed using the pretreated data which were collected from ASAT. The annual trend or characteristics of the general information on ICAT were also analyzed.

• Journal of Internet Computing and Services
• /
• v.21 no.4
• /
• pp.97-107
• /
• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.