• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.103-113
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• 2014

This study designed and implemented a query model for consecutive analyses of dynamic multivariate graph data. First, the query model consists of two procedures; setting the discriminant function, and determining an alteration method. Second, the query model was implemented as a query system that consists of a query panel, a graph visualization panel, and a property panel. A Node-Link Diagram and the Force-Directed Graph Drawing algorithm were used for the visualization of the graph. The results of the queries are visually presented through the graph visualization panel. Finally, this study used the data of worldwide import & export data of small arms to verify our model. The significance of this research is in the fact that, through the model which is able to conduct consecutive analyses on dynamic graph data, it helps overcome the limitations of previous models which can only perform discrete analysis on dynamic data. This research is expected to contribute to future studies such as online decision making and complex network analysis, that use dynamic graph models.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.41-47
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• 2012

There are PFD(Process Flow Diagram) and P&ID(Piping and Instrument Diagram) for designing and managing chemical process efficiently. They provide the operation condition and equipment specifications of chemical process, but they do not provide the reliability of chemical process. Therefore, in this study, Reliability Flow Diagram(RFD) which provide the cycle and time of preventive maintenance has been developed using Directed Graph Analysis methodology. Directed Graph Analysis methodology is capable of assessing the reliability of chemical process. It models chemical process into Directed Graph with nodes and arcs and assesses the reliability of normal operation of chemical process by assessing Directed Graph sequential. In this paper, the chemical process reliability transition according to operation time was assessed. And then, Reliability Flow Diagram has been developed by inserting the result into P&ID. Like PFD and P&ID, Reliability Flow Diagram provide valuable and useful information for the design and management of chemical process.

• Korean Management Science Review
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• v.23 no.3
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• pp.63-75
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• 2006

There are many kinds of research on web graph, most of them are focus on the hyperlinked structure of the web graph. Well known results on the web graph are rich-get-richer phenomenon, small-world phenomenon, scale-free network, etc. In this paper, we define 3 new directed web graph, so called the Web Usage Pattern Graph (WUPG), that nodes represent web sites arid arcs between nodes represent a movement between two sites by users' browsing behavior. The data to constructing the WUPG, approximately 56,000 records, are gathered from some users' PCs. The results analysing the data summarized as follows : (i) extremely rich-get-richer phenomenon (ii) average path length between sites is significantly less than the previous one (iii) less external hyperlinks, more internal hyperlinks.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• Journal of The Korea Institute of Healthcare Architecture
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• v.21 no.3
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• pp.25-35
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• 2015

Purpose: Signs that are installed at unnoticeable places or that disconnect before the destination can bring errors of location information delivery. Therefore, this study aims to find out the spatial relation between structure of space and signs in the perspective of visual exposure possibility, operating arrangement and assesment by applying spatial structure theory. Methods: Effectiveness of organization of guidance signs was evaluated after the four way-finding factors(Plan Configuration, Sign System, Perceptual Access, Architectural Difference) that G.D.Weisman suggested were interpreted by spatial structure theory(J-Graph analysis, Space Syntax, Visual Graph Analysis) under the premise that it is closely related to the structure of space. Results: 1) Because the south corridor that connects each department of outpatient division is located in the hierarchy center of the space, and walking density is expected to be high, guidance signs need to be organized at the place with high integration value. 2) The depth to the destination space can be estimated through J-Graph analysis. The depth means a switch of direction, and the guidance signs are needed according to the number. 3) According to visibility graph analysis, visual exposure can be different in the same hierarchy unit space according to the shape of the flat surface. Based on these data, location adjustment of signs is possible, and the improvement effect can be estimated quantitatively. Implications: Spatial structure theory can be utilized to design and evaluate sign systems, and it helps to clearly understand the improvement effect. It is desirable to specify design and estimation of sign systems in the order of J-Graph analysis${\rightarrow}$Space Syntax Theory${\rightarrow}$visibility graph analysis.

• Journal of the Korean School Mathematics Society
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• v.25 no.1
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• pp.79-104
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• 2022

It is very important to help students learn by examining how well students solve math problems. Therefore, in this study, four methods(error analysis by problem type, schematization analysis, area graph analysis, and broken line graph analysis) were constructed to analyze how the connectivity between concepts of middle school functions affects the problem solving results. The students' learning situation was visually expressed to enable intuitive understanding. This analysis method makes it easy to understand the evaluation results of students. It can help students learn by understanding their learning situation. It will be useful in mathematics teaching and learning as it can help students to monitor their own problems and make a self-directed learning plan.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.65-67
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• 2004

Change of state of Channel between two wireless terminals which is caused by noise and multiple environmental conditions for happens frequently from the Wireles Network. So, When it is like that planning a wireless network protocol or performance analysis, it follows to change of state of time-varying channel and packet the analysis against a transmission efficiency is necessary. In this paper, analyzes transmission time of a packet and a packet in a time-varying and packet based Wireless Network. To reflecte the feature of the time-varying channel, we use a Signal Flow Graph model. From the model the mean of transmission time and the mean of queue length of the packet are analyzed in terms of the packet distribution function, the packet transmission service time, and the PER of the time-varying channel.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1107-1116
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• 2014

In this paper, we review recent developments in network analysis using the graph theory, and introduce ongoing research area with relevant theoretical results. In specific, we introduce basic notations in graph, and conditional and marginal approach in constructing the adjacency matrix. Also, we introduce the Marcenko-Pastur law, the Tracy-Widom law, the white Wishart distribution, and the spiked distribution. Finally, we mention the relationship between degrees and eigenvalues for the detection of hubs in a network.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1997.11a
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• pp.191-202
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• 1997

점점 과학의 고도화가 이루어지면서 산업 현장에서 사용하는 장비나 플랜트의 대형화, 복잡화가 이루어지고 있다. 이에 이러한 시스템들의 정량적 위험성 평가가 요구하기 시작했고, 이를 계산하기 위해서 ETA, FTA, CCA등 여러 가지 방법들이 사용되고 있다. 그 중에서도 가장 보편적으로 많이 사용하는 방법으로 FTA(Fault Tree Analysis)를 손꼽을 수 있다. 관찰하는 시스템이 커지는 경우 FTA를 수행하기 위해서는 복잡한 계산 과정을 거치면서 많은 항의 계산을 하게 된다. 이러한 계산시간을 줄이기 위해서 많은 노력들이 있었으며 이중 하나는 FT를 graph(acyclic nonliner graph)로 변형시킨 후 graph이론을 사용하여 신뢰도를 계산하는 것이다. (중략)