• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.1034-1044
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• 2016

This study seeks a method to objectively evaluate sensibility based on Big Data in the field of design. In order to do so, this study examined the sensibility responses on design factors for the public through a network analysis of texts displayed in social media. 'Hanbok', a formal clothing that represents Korea, was selected as the subject for the research methodology. We then collected 47,677 keywords related to Hanbok from 12,000 posts on Naver blogs from January $1^{st}$ to December $31^{st}$ 2015 and that analyzed using social matrix (a Big Data analysis software) rather than using previous survey methods. We also derived 56 key-words related to design elements and sensibility responses of Hanbok. Centrality analysis and CONCOR analysis were conducted using Ucinet6. The visualization of the network text analysis allowed the categorization of the main design factors of Hanbok with evaluation terms that mean positive, negative, and neutral sensibility responses. We also derived key evaluation factors for Hanbok as fitting, rationality, trend, and uniqueness. The evaluation terms extracted based on natural language processing technologies of atypical data have validity as a scale for evaluation and are expected to be suitable for utilization in an index for sensibility evaluation that supplements the limits of previous surveys and statistical analysis methods. The network text analysis method used in this study provides new guidelines for the use of Big Data involving sensibility evaluation methods in the field of design.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• Journal of the Korea Computer Graphics Society
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• v.22 no.5
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• pp.27-36
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• 2016

As big data become more complex than ever, there has been a need for various techniques and approaches to better analyze and explore such big data. A research discipline of visual analytics has been proposed to help users' visual data analysis and decision-making. Since 2006 when the first symposium of visual analytics was held, the visual analytics research has become popular as the advanced technology in computer graphics, data mining, and human-computer interaction has been incorporated in visual analytics. In this work we introduce the visual analytics research by reviewing and surveying the papers published in IEEE VAST 2015 in terms of data and visualization techniques to help domestics researchers' understanding on visual analytics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.117-126
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• 2019

The data generated in the IoT environment is very diverse. Especially, the development of the fourth industrial revolution has made it possible to increase the number of fixed and unstructured data generated in manufacturing facilities such as Smart Factory. With Big Data related solutions, it is possible to collect, store, process, analyze and visualize various large volumes of data quickly and accurately. Therefore, in this paper, we will directly generate data using Raspberry Pi used in IoT environment, and analyze using various Big Data solutions. Collected by using an Sqoop solution collected and stored in the database to the HDFS, and the process is to process the data by using the solutions available Hive parallel processing is associated with Hadoop. Finally, the analysis and visualization of the processed data via the R programming will be used universally to end verification.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.143-146
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• 2016

본 연구에서는 대용량의 야구 데이터를 R 패키지인 googleVis를 이용하여 시각화하는 웹페이지를 구축하고, 버블 차트로 시각화하여 표현하였다. 웹페이지에서는 시각화하는 객체를 버블로 나타내며, 객체는 타자, 투수, 팀 3가지이다. 각 객체의 속성들을 버블 색상, 버블 사이즈, X-Y좌표, 연도에 설정함으로써 5차원으로 시각화하여 표현할 수 있게 한다. 웹페이지 기능 중 타임슬립 애니메이션을 사용하여 시간의 흐름에 따른 기록 변화를 한 눈에 관찰할 수 있으며, 선수 검색 기능을 통해 특정 선수들을 선택하여 비교 및 분석하는 것이 가능하다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.151-154
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• 2021

Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study was analyzed for 21 papers in the March 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 305 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• 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.

• 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 Korean Society for Library and Information Science
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• v.51 no.1
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• pp.271-287
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• 2017

This study aims to conduct a big data analysis of public library operations and uses of 16 metropolitan local government of Korea by using the Chernoff face method. This study is the first to use the Chernoff face method for big data analysis of library services in library and information research. The association of variables and human facial features was decided by survey. The study reveals that in general the provincial governments in Korea operate more libraries, invest more budgets, allocate more staff and hold more collections than metropolitan cities. This administration resulted in more use of libraries in provincial governments than metropolitan cities.

• Journal of KIBIM
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• v.12 no.4
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• pp.1-18
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• 2022

New Fabrication Facility (FAB) construction is performed with Building Information Modeling (BIM) based design. The BIM design data keep updated during the FAB construction. To improve fast-track construction management, a Fabrication Facility Augmented Reality (FABAR) was developed. This study introduces a FABAR system development process and shows performance evaluation results of the FABAR prototype system. The FABAR is implemented with three major modules: Augmented Reality (AR) visualization unit (Room-box) to transfer big BIM data to AR data, AR registration and tracking unit to match AR with real scape and to keep AR coordination in real, and AR data management unit to enhance usability. The prototype performance results were as follows: visualization of design BIM data via AR within 24 hours, precise AR registration and tracking registration, and appropriate usability to support FAB construction management at site. The results indicate that the FABAR is applicable for FAB construction management. Especially, the BIM data transformation method using Room-box in this study signifies a new construction management approach using fluctuating BIM design data in the fast track construction condition.