• International Journal of Contents
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• v.10 no.1
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• pp.29-35
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• 2014

This paper proposes an approach for visualizing research trends using theme maps and extra information. The proposed algorithm includes the following steps. First, text mining is used to construct a vector space of keywords. Second, correspondence analysis is employed to reduce high-dimensionality and to express relationships between documents and keywords. Third, kernel density estimation is applied in order to generate three-dimensional data that can show the concentration of the set of documents. Fourth, a cartographical concept is adapted for visualizing research trends. Finally, relative vitalization information is provided for more accurate research trend analysis. The algorithm of the proposed approach is tested using papers about Traditional Korean Medicine.

• Educational Technology International
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• v.7 no.1
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• pp.21-37
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• 2006

This paper focuses on finding out visualization process by means of VTA(Visual Task Analysis) of expert teachers' simple task. Findings indicate teachers have coding schema of performing visual task as such; (a) the analyzing by reading and some activities in the task text, (b) conceptualizing or understanding in his/her own way, (c) the explaining of the action or product, (d) the searching by thinking or finding, (e) the decision of visualizing of the task. Expert teachers tried to visualize in the form of abstract graph, and to omit the object which was not directly related to the topic at the pilot study. VAT based on ground theory and protocol analysis was developed and performed. This case study suggests that an additional study for searching a guide and method might be beneficial for conducting a visual task analysis.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.331-337
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• 2016

The purpose of this study is to provide a method to visualize the sound data to the three-dimensional image. The visualization of the sound data is performed according to the algorithm set after production of the text-based script that form the channel range of the sound data. The algorithm consists of a total of five levels, including setting sound channel range, setting picture frame for sound visualization, setting 3D image unit's property, extracting channel range of sound data and sound visualization, 3D visualization is performed with at least an operation signal input by the input device such as a mouse. With the sound files with the amount an animator can not finish in the normal way, 3D visualization method proposed in this study was highlighted that the low-cost, highly efficient way to produce creative artistic image by comparing the working time the animator with a study presented method and time for work. Future research will be the real-time visualization method of the sound data in a way that is going through a rendering process in the game engine.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.891-897
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• 2022

In big data analysis, raw text data mostly exists in various unstructured data forms, so it becomes a structured data form that can be analyzed only after undergoing heuristic pre-processing and computer post-processing cleansing. Therefore, in this study, unnecessary elements are purified through pre-processing of the collected raw data in order to apply the wordcloud of R program, which is one of the text data analysis techniques, and stopwords are removed in the post-processing process. Then, a case study of wordcloud analysis was conducted, which calculates the frequency of occurrence of words and expresses words with high frequency as key issues. In this study, to improve the problems of the "nested stopword source code" method, which is the existing stopword processing method, using the word cloud technique of R, we propose the use of "general stopword corpus" and "user-defined stopword corpus" and conduct case analysis. The advantages and disadvantages of the proposed "unstructured data cleansing process model" are comparatively verified and presented, and the practical application of word cloud visualization analysis using the "proposed external corpus cleansing technique" is presented.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2001.11a
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• pp.83-84
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• 2001

No abstract, See Full-text

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

• Journal of the Korean Society for Library and Information Science
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• v.50 no.2
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• pp.183-196
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• 2016

The academic journal is an important medium carrying newly discovered knowledge in various disciplines. It is desirable to consider visualization of journal and article information in order to make the information more insightful and effective than text-based information. In this study, visualization service platform of journal and article information is developed. TagCloud were included in both Infographics of journal and article. Each word in the TagCloud is inter-linked with DBPedia using Linked Open Data (LOD) technique.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.77-86
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• 2024

As the significance of knowledge convergence continues to grow, universities are making efforts to develop methods that promote multidisciplinary learning. To address this educational challenge, our paper applies network theory and text mining techniques to analyze university curricula and introduces a graphical syllabus rendering method. Visualizing the course curriculum provides a macro and structured perspective for individuals seeking alternative educational pathways within the existing system. By visualizing the relationships among courses, students can explore different combinations of courses with comprehensive search support. To illustrate our approach, we conduct a detailed demonstration using the syllabus database of Yonsei University. Through the application of our methods, we create visual course networks that reveal the underlying structure of the university curriculum. Our results yield insights into the interconnectedness of courses across various academic majors at Yonsei University. We present both macro visualizations, covering 18 academic majors, and visualizations for a few selected majors. Our analysis using Yonsei University's database not only showcases the value of our methodology but also serves as a practical example of how our approach can facilitate multidisciplinary learning.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.279-285
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• 2023

This study provides academic implications by considering trends of domestic research regarding therapy for Mental disorder schizophrenia and psychosocial. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 65 papers have been collected The result of this study is as follows. First, collected data were visualized through analysis of keywords by using word cloud method. Second, keywords such as intervention, schizophrenia, research, patients, program, effect, society, mind, ability, function were recorded with highest frequency resulted from keyword frequency analysis. Third, LDA (latent Dirichlet allocation) topic modeling result showed that classified into 3 keywords: patient, subjects, intervention of psychosocial, efficacy of interventions. Fourth, the social network analysis results derived connectivity, closeness centrality, betweennes centrality. In conclusion, this study presents significant results as it provided basic rehabilitation data for schizophrenia and psychosocial therapy through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of schizophrenia and psychosocial therapy through text mining and social network analysis.