• Journal of Information Management
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• v.42 no.2
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• pp.85-102
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• 2011

Conventionally, scientific data visualization is thought of as one of activities performed by scientists during the scientific data analysis. However, recently, there exits a set of research papers which count scientific data visualization as a independent research area. They show the research subjects for studying the scientific data visualization technology and methods. In case, a scientist or group of scientists can not solve their own visualization problem due to the unskillfulness and inexperience on using visualization tool. Therefore, it needs to help them by the systematic way for solving the problem. In this study, we analyze and propose the national level scientific visualization support system for scientists. In particular, we first analyze the existing papers and find out the critical success factors. Then, by integrating the findings of the analysis, we propose the research areas which need to be focused, and the strategic direction and specific research topics for scientific data visualization support system in national level.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1301-1311
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• 2017

Manufacturing big data systems have supported decision making that can improve preemptive manufacturing activities through collection, storage, management, and predictive analysis of related 4M data in pre-manufacturing processes. Effective visualization of data is crucial for efficient management and operation of data in these systems. This paper presents visualization techniques that can be used to effectively show data collection, analysis, and prediction results in the manufacturing big data systems. Through the visualization technique presented in this paper, we have confirmed that it was not only easy to identify the problems that occurred at the manufacturing site, but also it was very useful to reply to these problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2305-2311
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• 2017

In recent years, there have been tries to open public data from various government agencies along with publicization of public information for the public interest. In other words, various kinds of electronic data generated and collected by the public institutions as a result of their work are opened in the public portal sites. However, users who use it are limited in their use of big data due to lack of understanding of data format, lack of data processing knowledge, difficulty in accessing and managing data, and lack of visualization data to understand collected and stored data. Therefore, in this study, we propose a big data collection, storing and visualization platform that can collect big data provided by various public sites using data set URL and API regardless of data format, re-process collected data using XML structure.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1260-1263
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• 2013

데이터 시각화 기술을 이용하여 데이터에 대한 접근성과 이해도를 높여 사용자가 빅데이터 가치를 효율적으로 활용할 수 있도록 소셜 네트워크에서의 사용자 관계 시각화 기술에 대해 연구하였다. 본 논문은 소셜 네트워크의 사용자관계를 시각화하기 위해 소셜 네트워크 데이터 스트리밍 기술, 동적 애니메이션 적용을 위한 시각화 기술 그리고 사용자 관계를 분석하기 위한 데이터마이닝 기술을 적용하여 소셜 네트워크 사용자들 간의 관계를 본인 중심으로 시각화하는 방안을 제시하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1492-1494
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• 2011

최근 지구온난화에 따른 기후변화 현상에 대한 관심이 증가되고 있고 기후변화 현상을 Open Virtual Globes상에서 시각화하기 위한 연구가 활발히 진행되고 있다. 기후관련 데이터는 지상관측데이터, 위성 데이터, 수치모델데이터 등이다. 각 데이터를 시각화하는 소프트웨어를 분석하고 Virtual Golbes상에서 효과적으로 시각화 하기위한 기법을 설계하였다. 기후변화의 과학적 시각화를 통해 지구온난화에 대한 경각심을 높이는데 목적이 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.581-584
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• 2009

본 논문에서는 시간의 축에 따라 점진적으로 확산되는 정보 네트워크를 효과적으로 표현할 수 있는 시각화 도구를 소개하고자 한다. 이것은 기존의 정적인 데이터에 대한 정적인 시각화 결과물을 제시하는 시각화 도구들과는 달리, 시간의 축에 따라 동적으로 성장하는 데이터를 효과적으로 표현할 수 있다는데 그 차이가 있다. 이를 위해 그래프 기반의 정보 확산 네트워크 모델을 정의 하고, 이 그래프에 시간의 축에 따른 데이터의 성장을 반영하여, 정보 확산 과정을 시각화 할 수 있는 도구를 제작하였다. 결론적으로, 본 논문에서 제시하는 도구는 시간의 축에 따라 점진적으로 성장하는 정보 네트워크의 성장 과정을 시각화함으로 해서, 복잡하게 엉켜 있는 정보들이 어떻게 확산되며, 어떤 모양으로 생겼는지, 또 어떻게 진화하는가에 대한 시각적 재현을 통해 정보 확산 네트워크의 속성을 이해하는데 도움을 줄 수 있는 새로운 시각화 도구를 제시했다는데 그 의미가 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.21-22
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• 2021

최근 AI(Artificial Intelligence)를 기반으로 정밀한 가스 성분 감지를 위한 후각지능(Olfactory intelligence) 기술에 연구가 활발히 진행 중이다. 후각지능 학습데이터는 다른 감지 방식의 가스 센서들이 동시에 적용되는 멀티모달리티의 특성을 지니며 또한, 공간상에 분포된 센서 배열을 통해 획득된 다차원의 시계열 특성을 지닌다. 따라서 대량의 다차원 데이터에 대한 정확한 이해와 분석을 위해서는 데이터를 전처리하고 시각화할 수 있는 기술이 필요하다. 본 논문에서는 후각지능 학습을 위한 다차원의 복잡한 가스 데이터의 시각화를 위해 잡음 등의 불필요한 값을 제거하고, 데이터가 일관성을 가지도록 하며, 데이터의 차원을 시각화 가능하도록 축소하기 위한 전처리 방법을 제시한다.

• Spatial Information Research
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• v.23 no.6
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• pp.109-116
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• 2015

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.763-770
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• 2024

Currently, land-based fish farms utilizing seawater have introduced and are utilizing various equipment such as real-time water quality monitoring systems, facility automation systems, and automated dissolved oxygen supply devices. Furthermore, data collected from various equipment in these fish farms produce structured and unstructured big data related to water quality environment, facility operations, and workplace visual information. The big data generated in the operational environment of fish farms aims to improve operational and production efficiency through the development and application of various methods. This study aims to develop a system for effectively analyzing and visualizing big data produced from land-based fish farms. It proposes a data visualization process suitable for use in a fish farm big data analysis system, develops big data visualization tools, and compares the results. Additionally, it presents intuitive visualization models for exploring and comparing big data with time-series characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.393-402
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• 2015

Data visualization technology helps people easily understand various data and its analysis result, so usefulness of it is expected in the real industrial manufacturing sites. The large amount of data which is occurred at the manufacturing sites is able to fulfill very important roll to improve the manufacturing process. In this paper, we propose an information visualization for the manufacturing process optimization based on design of experimental and data analysis. The manufacturing process may be improved and be reduced cause of faulty by providing the easy-process analysis to understand the operation site through the information visualization of data analysis result.