• Journal of information and communication convergence engineering
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• v.3 no.1
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• pp.49-55
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• 2005

This study is to suggest a visualization technique to display the relations of associated data in an optimal way when trying to display the whole data on a limited space by dealing with a large amount of data with linked information. For example, if you track an IP address through several steps and display the data on a screen, or if you visualize the human gene information on a 3-dimensional space, then it becomes even easier to understand the data flow in such cases. In order to simulate the technique given in this study, the given algorithm was applied to a large number of nodes made in a random fashion to optimize the data and we visually observed the result. According to the result, the technique given in this study is more efficient than any previous method in terms of visualization and utilizing space and allows to more easily understand the whole structure of a node because it consists of sub-groups.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.465-474
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• 2004

Data visualization is the automation process and the discovery process to data sets in an effort to discover underlying information from the data. It provides rich visual depictions of the data. It has distinct advantages over traditional data analysis techniques such as exploring the structure of large scale data set both in the sense of number of observations and the number of variables by allowing great interaction with the data and end-user. We discuss the principles of data visualization and evaluate the characteristics of various tools of visualization according to these principles.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.421-428
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• 2009

On the visualization of categorical data, if the number of categories is small, we can consider Hayashi Quantification Method 3 for visualization of the categories of the variables. But it is known that the method is unstable because it quantifies more significantly for the small frequency categories rather than large frequency categories. The purpose of this research is to propose the visualization of large two-way crosstabulation data by PLS methods for checking the relationship between the categories of row and column variables. In this research, we utilize the PLS visualization methods (Huh et al., 2007) that is proposed for visualization of the qualitative data to visualize the categories of the large categorical data. We also compared both methods by applying them to real data, and studied the results from PLS visualization method on the real categorized data with many categories.

• Journal of the Korean Society of Visualization
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• v.1 no.2
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• pp.58-70
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• 2003

Meteorological data contains observation and numerical weather prediction model output data. The computerized analysis and visualization of meteorological data often requires very high computing capability due to the large size and complex structure of the data. Because the meteorological data is frequently formed in multi-variables, 3-dimensional and time-series form, it is very important to visualize and analyze the data in 3D spatial domain in order to get more understanding about the meteorological phenomena. In this research, we developed interactive 3-dimensional visualization techniques for visualizing meteorological data on a PC environment such as volume rendering, iso-surface rendering or stream line. The visualization techniques developed in this research are expected to be effectively used as basic technologies not only for deeper understanding and more exact prediction about meteorological environments but also for scientific and spatial data visualization research in any field from which three dimensional data comes out such as oceanography, earth science, and aeronautical engineering.

• International Journal of Air-Conditioning and Refrigeration
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• v.15 no.4
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• pp.156-162
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• 2007

Flow visualization experiments were conducted for two louver arrays having large louver pitch ratio ($L_p/F_p=1.0$ and 1.4). Flow efficiencies and critical Reynolds numbers were obtained from the data, and were compared with existing correlations. The correlations failed to predict the present flow efficiency data adequately; some correlation overpredicted the data, while others underpredicted the data. Large louver pitch ratio of the present model, which is outside of the applicable range of the correlations may partly be responsible. The critical Reynolds numbers obtained from the present flow visualization data were in close agreement with those obtained from the heat transfer tests on actual flat tube heat exchangers. Existing correlations on the critical Reynolds number generally overpredicted the present data.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.54-61
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• 2023

The family tree is one of the key elements of humanities classics research and is very important for accurately understanding people or families. In this paper, we introduce a method for automatically generating a family tree using information on interpersonal relationships (IIPR) from the Korean Classics Database (KCDB) and visualize interpersonal searches within a family tree using data-driven document JavaScript (d3.js). To date, researchers of humanities classics have wasted considerable time manually drawing family trees to understand people's influence relationships. An automatic family tree builder analyzes a database that visually expresses the desired family tree. Because a family tree contains a large amount of data, we analyze the performance and bottlenecks according to the amount of data for visualization and propose an optimal way to construct a family tree. To this end, we create an n-ary tree with fake data, visualize it, and analyze its performance using simulation results.

• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.11-18
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• 1997

Scientific volume visualization addresses the representation, manipulation, and rendering of volumetric data sets, providing mechanisms for looking closely into structures and understanding their complexity and dynamics. In the past several years, a tremendous amount of research and development has been directed toward algorithms and data modeling methods for a scientific data visualization. But there has been very little work on developing a mathematical volume model that feeds this visualization. Especially, in flow visualization, the volume model has long been required as a guidance to display the very large amounts of data resulting from numerical simulations. In this paper, we focus on the mathematical representation of volumetric data sets and the method of extracting meaningful information from the derived volume model. For this purpose, a B-spline volume is extended to a high dimensional trivariate model which is called as a flow visualization model in this paper. Two three-dimensional examples are presented to demonstrate the capabilities of this model.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1061-1075
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• 2016

In this paper, we discuss various methods to visualize high dimensional large-scale data and review some issues associated with visualizing this type of data. High-dimensional data can be presented in a 2-dimensional space with a few selected important variables. We can visualize more variables with various aesthetic attributes in graphics or use the projection pursuit method to find an interesting low-dimensional view. For large-scale data, we discuss jittering and alpha blending methods that solve any problem with overlapping points. We also review the R package tabplot, scagnostics, and other R packages for interactive web application with visualization.

• Information Systems Review
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• v.5 no.1
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• pp.49-60
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• 2003

The purpose of this study is to empirically and practically verify the applicability of visualization data mining tool in detecting real-word insurance frauds that are now emerged as one of the most serious problems socially and economically. For the verification, Analyst's Notebook by i2, which has been known as the most effective visualization data mining tool, was adopted. With Analyst's Notebook, fraud-probable insurance transactions from a very large insurance claims are selected and then substantiation for insurance frauds are attempted. The results show that Analyst's Notebook not only detects insurance fraud transactions from a vast number of insurance claims, but is also able to pinpoint organized crime group by associating one fraud transaction to another fraud transaction. Therefore, it is safe to conclude that visualization data mining is very effective in detecting false transactions and crime behaviors including insurance fraud.

• Journal of the HCI Society of Korea
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• v.3 no.2
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• pp.1-8
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• 2008

We introduce VRSMS(VR-based sensor management system) which is the visualization system of micro-scale air quality monitoring system Airscope[3]. By adopting VR-based visualization method, casual users can get insight of air quality data intuitively. Users can also manipulate sensors in VR space to get specific data they needed. For adaptive visualization, we separated visualization and interaction methods from air quality data. By separation, we can get consistent way for data access so new visualization and interaction methods are easily attached. As one of the adaptive visualization method, we constructed large display system which consists of several small displays. This system can provide accessibility for air quality data to people one public space.