• Title/Summary/Keyword: Data visualization

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A Guiding System of Visualization for Quantitative Bigdata Based on User Intention (사용자 의도 기반 정량적 빅데이터 시각화 가이드라인 툴)

  • Byun, Jung Yun;Park, Young B.
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.6
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    • pp.261-266
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    • 2016
  • Chart suggestion method provided by various existing data visualization tools makes chart recommendations without considering the user intention. Data visualization is not properly carried out and thus, unclear in some tools because they do not follow the segmented quantitative data classification policy. This paper provides a guideline that clearly classifies the quantitative input data and that effectively suggests charts based on user intention. The guideline is two-fold; the analysis guideline examines the quantitative data and the suggestion guideline recommends charts based on the input data type and the user intention. Following this guideline, we excluded charts in disagreement with the user intention and confirmed that the time user spends in the chart selection process has decreased.

A Study on the Supporting System for Scientific Data Visualization at the National Level (국가수준의 과학데이터 시각화 지원체계에 관한 연구)

  • Park, Dong-Jin;Chae, Kyun-Shik;Ryu, Beom-Jong;Lee, Sang-Tae
    • 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.

Case Studies on Deceptive Data Visualization (기만적 데이터 시각화 사례 연구)

  • Kim, Si-Hyun;Park, Jin-Wan
    • Journal of Digital Contents Society
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    • v.19 no.3
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    • pp.521-528
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    • 2018
  • Data visualization has become a useful tool to effectively communicate information and is widely used not only by experts but also at a general level. However, it is dangerous that it is as efficient as it is to transmit false information. All data visualizations have hidden intent with powerful messages by editor. Building a system that grasps these intentions helps to understand the thoughts of groups and individuals. Most of the existing research focuses on effective data visualization methods and methods of expression. The more various visualization methods, the more likely the data will be distorted. In this paper, we present an analysis of deceptive data visualization in a goal-oriented environment. Based on the vulnerability of human cognitive processing, we classify the attack types and identify what tricks occur in the context of data visualization. This study suggests the first step in studying the case of aggressive visualization and opens the way for further research.

Information Visualization Process for Spatial Big Data (공간빅데이터를 위한 정보 시각화 방법)

  • Seo, Yang Mo;Kim, Won Kyun
    • 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.

Survey on the LIC based flow visualization (LIC 기반의 유동 가시화 기법에 대한 조사 연구)

  • Lee, Joong-Youn
    • Proceedings of the Korea Contents Association Conference
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    • 2007.11a
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    • pp.530-534
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    • 2007
  • Flow visualization is one of visualization techniques and it means a visual expression of vector data using 2D or 3D graphics. It aims for human to easily understand a special feature of the vector data. Flow visualization can be classified into various criterions such as visualization technique, data dimension, type of the flow, and so on. Visualization technique can be categorized into direct method, integration method and derived data based method. Data dimension can be divided into 2D, 2.5D and 3D. Type of flow data may be classified into steady and unsteady. In this paper, various LIC based flow visualization methods will be introduced which is one of representative integration based techniques. Those methods will be categorized with more detailed criterions such as dimension and type of flows.

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DEVELOPMENT OF ON-LINE DATA VISUALIZATION PROGRAM AND ITS APPLICATION (온-라인 데이터 가시화 프로그램의 개발과 그 적용)

  • Kang, S.H.;Kim, B.S.
    • 한국전산유체공학회:학술대회논문집
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    • 2008.03a
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    • pp.290-296
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    • 2008
  • In this paper development of on-line data visualization program is described and some examples of data postprocessing are shown. The program is written in JAVA language and runs as a JAVA applet on the web browser such as Internet Explorer or Firefox. Remote users can use the program to visualize and analyze their own flow data by accessing the program server through the internet and loading data files in proper formats from their local computers. This paper describes briefly about algorithms for data visualization, structure and available functions of the program, and web sever system. The mechanism of how the JAVA applet can access and process local data files and relevant coding techniques are explained as well. Also explained is what is required for the remote users and client computers to access the program on-line. Some visualization examples performed on a local computer are illustrated by accessing the server remotely.

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DEVELOPMENT OF ON-LINE DATA VISUALIZATION PROGRAM AND ITS APPLICATION (온-라인 데이터 가시화 프로그램의 개발과 그 적용)

  • Kang, S.H.;Kim, B.S.
    • 한국전산유체공학회:학술대회논문집
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    • 2008.10a
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    • pp.290-296
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    • 2008
  • In this paper development of on-line data visualization program is described and some examples of data postprocessing are shown. The program is written in JAVA language and runs as a JAVA applet on the web browser such as Internet Explorer or Firefox. Remote users can use the program to visualize and analyze their own flow data by accessing the program server through the internet and loading data files in proper formats from their local computers. This paper describes briefly about algorithms for data visualization, structure and available functions of the program, and web sever system. The mechanism of how the JAVA applet can access and process local data files and relevant coding techniques are explained as well. Also explained is what is required for the remote users and client computers to access the program on-line. Some visualization examples performed on a local computer are illustrated by accessing the server remotely.

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Analysis of Computational Science and Engineering SW Data Format for Multi-physics and Visualization

  • Ryu, Gimyeong;Kim, Jaesung;Lee, Jongsuk Ruth
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.2
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    • pp.889-906
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    • 2020
  • Analysis of multi-physics systems and the visualization of simulation data are crucial and difficult in computational science and engineering. In Korea, Korea Institute of Science and Technology Information KISTI developed EDISON, a web-based computational science simulation platform, and it is now the ninth year since the service started. Hitherto, the EDISON platform has focused on providing a robust simulation environment and various computational science analysis tools. However, owing to the increasing issues in collaborative research, data format standardization has become more important. In addition, as the visualization of simulation data becomes more important for users to understand, the necessity of analyzing input / output data information for each software is increased. Therefore, it is necessary to organize the data format and metadata for the representative software provided by EDISON. In this paper, we analyzed computational fluid dynamics (CFD) and computational structural dynamics (CSD) simulation software in the field of mechanical engineering where several physical phenomena (fluids, solids, etc.) are complex. Additionally, in order to visualize various simulation result data, we used existing web visualization tools developed by third parties. In conclusion, based on the analysis of these data formats, it is possible to provide a foundation of multi-physics and a web-based visualization environment, which will enable users to focus on simulation more conveniently.

Development of Web-based Interface Tool for Map Data Visualization (웹 환경에서의 지도 기반 데이터 시각화 인터페이스 툴 개발)

  • Choi, Jin;Kil, Sun-Young;Lim, Soon-Bum
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1216-1223
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    • 2017
  • Because of the open data in public domains, it is easy to collect the public data. However, people find difficulty in visualizing the data in material that they actually want. Especially, A map is a difficult material to do the visualization work, without using the specific tools and learning. Therefore, in this paper, we proposed an interface tool for map data visualization that user can easily visualize various national statistics data on the map. We designed the interface by classifying the properties of the map systematically, focusing on the completion and convenience for making the map. After that, we developed a web-based application using D3.js. After user evaluation, we found that our application can visualize the map more quickly and completely than any other web interfaces for map data visualization. Users also found a higher satisfaction in operating convenience.

Idea proposal of InfograaS for Visualization of Public Big-data (공공 빅데이터의 시각화를 위한 InfograaS의 아이디어 제안)

  • Cha, Byung-Rae;Lee, Hyung-Ho;Sim, Su-Jeong;Kim, Jong-Won
    • Journal of Advanced Navigation Technology
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    • v.18 no.5
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    • pp.524-531
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    • 2014
  • In this paper, we have proposed the processing and analyzing the linked open data (LOD), a kind of big-data, using resources of cloud computing. The LOD is web-based open data in order to share and recycle of public data. Specially, we defined the InfograaS (Info-graphic as a service), new business area of SaaS (software as a service), to support visualization technique for BA (business analytics) and Info-graphic. The goal of this study is easily to use it by the non-specialist and beginner without experts of visualization and business analysis. Data visualization is the process to represent visually and understand the data analysis easily. The purpose of data visualization is to deliver information clearly and effectively by chart and figure. The big data of public data are shared and presented in the charts and the graphics understood easily by various processing results using Hadoop, R, machine learning, and data mining of open source and resources of cloud computing.