• Journal of Game and Entertainment
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• v.2 no.2
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• pp.61-69
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• 2006

Along with advance in visual culture, the importance of motion graphic has been increasingly emphasized day by day, which can maximize information delivery using image illustration and typography, graphic factors of images. In addition, we can easily see increasing cases where what a designer intends is visualized using creative typography in diverse mass media such as TV commercials, movies or web. Thanks to the effects of this trend, various ways of manufacturing works have been proposed in the field of motion typography by applying diverse factors including verbal ones, time, form, motion, colors, and sound for the purpose of expressing formless semantic notions through visual form of typography. However, physiological features such as sex, age, health status, pathological conditions, and body size can have a bigger effect on the process of real communication. Therefore, if property of quasi-verbal sound can be reflected appropriately in motion typography where communication is expressed only by visual form, it can enable people to understand what a designer intends faster and more exactly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1199-1205
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• 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 Information Science Theory and Practice
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• v.9 no.3
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• pp.76-106
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• 2021

The study analyzed bibliometric data of papers published in Korea Citation Index (KCI) and Web of Science (WoS) journals from 2002 to 2021. After examining size differences of KCI and WoS domains in the number of authors, institutions, and journals to put publication and citations counts in perspective, the study investigated co-authorship patterns over time to compare collaboration trends of Korean and international scholars and analyzed the data at author, institution, and journal levels to explore how the influences of authors, institutions, and journals on research output differ across domains. The study also conducted frequency-based analysis of keywords to identify key topics and visualized keyword clusters to examine topic trends. The result showed Korean LIS authors to be twice as productive as international authors but much less impactful and Korean institutions to be at comparable levels of productivity and impact in contrast to much of productivity and impact concentrated in top international institutions. Citations to journals exhibited initially increasing pattern followed by a decreasing trend though WoS journals showed far more variance than KCI journals. Co-authorship trends were much more pronounced among international publication, where larger collaboration groups suggested multi-disciplinary and complex nature of international LIS research. Keyword analysis found continuing diversification of topics in international research compared to relatively static topic trend in Korea. Keyword visualization showed WoS keyword clusters to be much denser and diverse than KCI clusters. In addition, key keyword clusters of WoS were quite different from each other unlike KCI clusters which were similar.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.35-43
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• 2019

This paper is a study on the improvement of industrial land information system. Status and problems of current industrial land information system is analyzed to establish objectives and methods and direction of improving the system based on our analysis. Since the industrial land information system is using outdated H/W and commercial S/W, there exists problems such as large maintenance cost, difficulty of immediate response for system problems and limited access of searching and providing information. In this paper, the problems of current system is resolved through applying the "e-Government standard framework" and open source S/W. And we suggested application of functions on information link enhancement and visualization through this research. Our research is expected to be applicable for analyzing/designing and developing system improvement in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.1-9
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• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• Journal of Contemporary Eastern Asia
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• v.18 no.1
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• pp.7-29
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• 2019

This study describes an approach for comparative bibliometric analysis of scientific publications related to (i) individual or several departments comprising a university, and (ii) broader integrated subject areas using multiple disciplinary schemes. It uses a custom dataset of scientific publications (ca. 15,000 articles and reviews, published during 2009-2013, and recorded in the Web of Science Core Collections) with author affiliations to the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university. The dataset was subjected, at first, to the department level and discipline level analyses using the newly available KAKEN-L3 classification (based on MEXT/JSPS Grants-in-Aid system), hierarchical clustering, correspondence analysis to decipher the major departmental and disciplinary clusters, and visualization of the department-discipline relationships using two-dimensional stacked bar diagrams. The next step involved the creation of subsets covering integrated subject areas and a comparative analysis of departmental contributions to a specific area (medical, health and life science) using several disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas, OECD Frascati 38 subordinate research fields, and KAKEN-L3 66 subject categories. To illustrate the effective use of the science mapping techniques, the same subset for medical, health and life science area was subjected to network analyses for co-occurrences of keywords, bibliographic coupling of the publication sources, and co-citation of sources in the reference lists. The science mapping approach demonstrates the ways to extract information on the prolific research themes, the most frequently used journals for publishing research findings, and the knowledge base underlying the research activities covered by the publications concerned.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.375-380
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• 2022

The purpose of this study is to find out consumers' perception and meaning of convenience store convenience food by using big data. For this study, NNAVER and Daum analyzed news, intellectuals, blogs, cafes, intellectuals(tips), and web documents, and used 'convenience store convenience food' as keywords for data search. The data analysis period was selected as 3 years from January 1, 2019 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted using TEXTOM, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, convenience store convenience foods were clustered into health, diversity, convenience, and economy according to consumers' selection attributes. It is expected to be the basis for the development of a new convenience menu that pursues convenience and convenience based on consumers' meaning of convenience store convenience foods such as appropriate prices, discount coupons, and events.

• Women's Health Nursing
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• v.29 no.1
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• pp.20-31
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• 2023

Purpose: As more newborns have received expanded newborn screening (NBS) for metabolic disorders, the overall number of false-positive results has increased. The purpose of this study was to explore the psychological impacts experienced by mothers related to the NBS process. Methods: An online parenting community in Korea was selected, and questions regarding NBS were collected using web crawling for the period from October 2018 to August 2021. In total, 634 posts were analyzed. The collected unstructured text data were preprocessed, and keyword analysis, topic modeling, and visualization were performed. Results: Of 1,057 words extracted from posts, the top keyword based on 'term frequency-inverse document frequency' values was "hypothyroidism," followed by "discharge," "close examination," "thyroid-stimulating hormone levels," and "jaundice." The top keyword based on the simple frequency of appearance was "XXX hospital," followed by "close examination," "discharge," "breastfeeding," "hypothyroidism," and "professor." As a result of LDA topic modeling, posts related to inborn errors of metabolism (IEMs) were classified into four main themes: "confirmatory tests of IEMs," "mother and newborn with thyroid function problems," "retests of IEMs," and "feeding related to IEMs." Mothers experienced substantial frustration, stress, and anxiety when they received positive NBS results. Conclusion: The online parenting community played an important role in acquiring and sharing information, as well as psychological support related to NBS in newborn mothers. Nurses can use this study's findings to develop timely and evidence-based information for parents whose children receive positive NBS results to reduce the negative psychological impact.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.737-751
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• 2023

The importance of digital forensics in the cloud environment is increasing as businesses and individuals move their data from On-premise to the cloud. Cloud data can be stored on various devices, including mobile devices and desktops, and encompasses a variety of user behavior artifacts, such as information generated from linked accounts and cloud services. However, there are limitations in securing and analyzing digital evidence due to environmental constraints of the cloud, such as distributed storage of data and lack of artifact linkage. One solution to address this is archiving services, and Google's Takeout is prime example. In this paper, user behavior data is analyzed for cloud forensics based on archiving data and necessary items are selected from an investigation perspective. Additionally, we propose the process of analyzing selectively collected data based on time information and utilizing web-based visualization to meaningfully assess artifact associations and multi-behaviors. Through this, we aim to demonstrate the value of utilizing archiving data in response to the increasing significance of evidence collection for cloud data.