• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.131-140
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• 2009

Recently, the information visualization research community has given significant attention to graph visualization, especially visualization of social networks. However, visualization of information flows in a very large social network has not been studied in depth. However, information flows are tightly related to the structure of social networks and it shows dynamic behavior of interactions between members of social networks. Thus, we can get much useful information about social networks from information flows. In this paper, we present our research result that enables users to navigate a very large social network in Google Maps' method and to take a look at information flows on the network. To this end, we devise three techniques; (i) mapping a very large social network to a 2-dimensional graph layout, (ii) exploring the graph to all directions with zooming it in/out, and (iii) building an efficient query processing framework. With these methods, we can visualize very large social networks and information flows in a limited display area with a limited computing resources.

• Design Convergence Study
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• v.14 no.4
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• pp.133-150
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• 2015

In this study, we proposed Interactive Visualization methods that can be analyzed relations between nations in various viewpoints such as period, issue using total 5211 of the UN General Assembly voting data.For this research, we devised a similarity matrix between nations and developed two visualization method based similarity matrix. The first one is Network Graph Visualization that can be showed relations between nations which participated in the vote of the UN General Assembly like Social Network Graph by year. and the second one is Proximity based Circular Visualization that can be analyzed relations between nations focus on one nation or Changes in voting patterns between nations according to time. This study have a great signification. that's because we proposed Proximity based Circular Visualization methods which merged Line and Circle Graph for network analysis that never tried from other cases of studies that utilize conventional voting data and made it. We also derived co-operatives of each visualization through conducting a comparative experiment for the two visualization. As a research result, we found that Proximity based Circular Visualization can be better analysis each node and Network Graph Visualization can be better analysis patterns for the nations.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1020-1023
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• 2008

멀티스레드 프로그램의 효과적인 디버깅을 위해서는 스레드의 비결정성에 의해 야기되는 다양한 수행 양상의 직관적인 이해가 중요하다. 스레드 수행 양상을 시각화하는 기존의 기법들은 공유 변수의 접근사건들 간의 부분 순서를 표현함으로써 시각적 복잡도가 높거나 이전 수행에서 결정된 락킹 순서를 표현하여 잠재되어 있는 다른 수행 양상에 대한 정보를 제공하지 못 한다. 본 논문은 프로그램 수행의 비결정적인 부분 순서는 락의 종류와 속성을 포함하는 코드 블록으로 시각화하고, 결정적인 부분 순서는 블록들을 연결하는 간선으로 시각화한다. 본 연구의 그래프는 플랫폼에 독립적인 Java Swing으로 구현하고 합성 프로그램을 사용하여 효과성을 실험한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.549-557
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• 2016

It is important for effective visualization to summarize not only a large amount of debugging information but also the mental models of abstract ideas. This paper presents an abstract visualization tool which provides effective visualization of thread structure and race information for OpenMP programs with critical sections and nested parallelism, using a partial order execution graph which captures logical concurrency among threads. This tool is supported by an on-the-fly trace-filtering technique to reduce space complexity of visualization information, and a graph abstraction technique to reduce visual complexity of nested parallelism and critical sections in the filtered trace. The graph abstraction of partial-order relation and race information is effective for understanding program execution and detecting to eliminate races, because the user can examine control flow of program and locations of races in a structural fashion.

• Journal of the HCI Society of Korea
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• v.10 no.1
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• pp.5-17
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• 2015

Nowadays various public data have been serviced to the public. Through the opening of public data, the transparency and effectiveness of public policy developed by governments are increased and users can lead to the growth of industry related to public data. Since end-users of using public data are citizens, it is very important for everyone to figure out the meaning of public data using proper visualization techniques. In this work, to indicate the significance of widespread public data, we consider UN voting record as public data in which many people may be interested. In general, it has high utilization value by diplomatic and educational purposes, and is available in public. If we use proper data mining and visualization algorithms, we can get an insight regarding the voting patterns of UN members. To visualize, it is necessary to measure the voting similarity values among UN members and then a social graph is created by the similarity values. Next, using a graph layout algorithm, the social graph is rendered on the screen. If we use the existing method for visualizing the social graph, it is hard to understand the meaning of the social graph because the graph is usually dense. To improve the weak point of the existing social graph visualization, we propose Friend-Matching, Friend-Rival Matching, and Bubble Heap algorithms in this paper. We also validate that our proposed algorithms can improve the quality of visualizing social graphs displayed by the existing method. Finally, our prototype system has been released in http://datalab.kunsan.ac.kr/politiz/un/. Please, see if it is useful in the aspect of public data utilization.

• The Journal of Bigdata
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• v.5 no.1
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• pp.77-88
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• 2020

Recently, many researches habe been done to organize and analyze various complex relationships in real world, represented in the form of graphs. In particular, the computer field literature data system, such as DBLP, is a representative graph data in which can be composed of papers, their authors, and citation among papers. Becasue graph data is very complex in storage structure and expression, it is very difficult task to search, analysis, and visualize a large size of bibliographic big data. In this paper, we develop a graphic user interface tool, called EEUM, which visualizes bibliographic big data in the form of graphs. EEUM provides the features to browse bibliographic big data according to the connected graph structure by visually displaying graph data, and implements search, management and analysis of the bibliographc big data. It also shows that EEUM can be conveniently used to search, explore, and analyze by applying EEUM to the bibliographic graph big data provided by DBLP. Through EEUM, you can easily find influential authors or papers in every research fields, and conveniently use it as a search and analysis tool for complex bibliographc big data, such as giving you a glimpse of all the relationships between several authors and papers.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.95-102
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• 2013

In metabolomics, metabolic pathway is represented by well-displayed graph. Metabolic pathways, especially, have a complex binding structure, which makes the graphical representation hard to visualize. There is a problem that edge crossings exponentially increase as the number of nodes grows. To apply automatic graph layout techniques to the genome-scale metabolic flow of metabolism domains, it is very important to reduce unnecessary edge crossing on a metabolic pathway layout. we proposed a metabolic pathway layout algorithm based on 2-layer layout. Our algorithm searches any meaningful component existing in a pathway, such as circular components, highly connected nodes, and the components are drawn in upper layer. Then the remaining subgraphs except meaningful components are drawn in lower layer by utilizing a new radial layout algorithm. It reduces ultimately reduced the number of edge crossings. This algorithm is the basis of flexible analysis for metabolic pathways.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.971-981
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• 2012

Google map has become one of the most recognized and comfortable means for providing statistical information of geographically referenced data. In this article, we introduce R functions to embed google map images on R interface and develop a function to represent statistical graphs such as bar graph, pie chart, and rectangle graph on a google map images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.347-348
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• 2016

스토리에 의해서 콘텐츠 산업의 경쟁력이 결정되고 콘텐츠에 대한 흥행과 보급에 핵심 요소가 된다. 현재까지의 스토리에 대한 연구는 대부분 인문학의 분야에서 이루어지고 있다. 인문학에서는 스토리를 창작하는 방법과 스토리에 대한 해석에 주로 초점이 맞추어져서 연구가 이루어지고 있다. 콘텐츠 산업의 핵심요소인 스토리를 창작하거나 해석하기 위해서는 스토리를 한눈에 보고 이해하거나 설명하기 위한 다양한 해석 방법과 시각화 방법이 필요하다. 따라서 본 논문에서는 스토리의 무대가 되는 공간 정보와 인물 정보가 함게 고려된 장면 그래프에 대한 연구를 제시하고 설명한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.167-176
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• 2023

In this paper, we designed and implemented a graph assessment module that can evaluate graphs in an programming assessment system based on text and numbers. The assessment method of the graph assessment module is self-evaluation that outputs two graphs generated by codes submitted by learners and by answers, automatic-evaluation that converts each graph image into an array, and gives feedback if it is wrong. The data used to generate the graph can be inputted directly or used from external data, and the method of generatng graph that can be evaluated is MATLAB style in matplotlib, and the graph shape that can be evaluated is presented in mathematics and curriculum. Through expert review, it was confirmed that the content elements of the assessment module, the possibility of learning, and the validity of the learner's needs were met. The graph assessment module developed in this study has expanded the evaluation area of the programming automatic asssessment system and is expected to help students learn data visualization.