• Journal of Digital Convergence
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• v.14 no.4
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• pp.251-258
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• 2016

Big data processing is a broad term for processing data sets so large or complex that traditional data processing applications are inadequate. Widespread use of smart devices results in a huge impact on the way we process data. Many organizations are contemplating how to incorporate or integrate those devices into their enterprise data systems. We have proposed a way to process big data by way of integrating Raspberry Pi into a Hadoop cluster as a computational grid. We have then shown the efficiency through several experiments and the ease of scaling of the proposed system.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.314-316
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• 2012

그리드 컴퓨팅 환경에서 세계의 많은 과학기술연구자들은 gLite 미들웨어 기반의 어플리케이션을 이용하고 있고 미들웨어에서 데이터를 관리, 분석하기 위해 메타데이터 카탈로그 기술인 AMGA 서비스를 이용하고 있다. 사용자들은 자신의 메타데이터와 데이터 파일을 디렉토리별로 접근하기 위해 유닉스 기반의 명령어와 AMGA 명령어만를 사용하여야 하는 불편함이 있었다. 이를 보완하기 위해 RCP 기반의 AMGA 그래픽 사용자 인터페이스를 2년 전부터 제공해 왔고, 보다 사용자에게 친숙하고 접근성이 용이한 웹 인터페이스를 설계하였다. 본 논문은 AMGA 서비스의 직관적이고 범용적인 엑세스를 위한 웹 그래픽 사용자 인터페이스를 기존의 RCP 모델의 컴포넌트 재사용으로 개발 기간의 단축과 유지보수의 용이성을 고려한 AMGA 웹 인터페스 구현 설계에 대해서 논한다.

• 한국전산유체공학회:학술대회논문집
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• 2002.10a
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• pp.71-77
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• 2002

The grid technology is believed to be the next generation research tool for both computational and experimental scientists. With advanced network technologies and middleware, geographically distributed facilities can be tightly connected to provided a huge amount of resources or remote accessibility, In this paper, an overview of grid technology will be introduced with an emphasis in application to computational fluid dynamics. The computational fluid dynamics, which involves solution of partial differential equations, is basically limited by the computing power, With the grid technology, virtually unlimited resources are provided. The schematic structure of middleware and grid environment, as well as some preliminary results are presented.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.339-343
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• 2007

최근의 의료 데이터는 대용량의 디지털 이미지로 생산된다. 이러한 대용량 이미지를 처리하기 위해서는 많은 처리 능력과 대량의 데이터 저장 공간이 필요하다. 현재 각 병원에서 생산되는 의료 이미지는 개별적으로 구축되어있는 PACS[3]에 저장하고 관리한다. 이러한 의료 환경속에서 대량의 데이터 저장공간 확보뿐 아니라 환자들의 중복 검사 방지, 의료 연구를 위한 풍부한 데이터 제공을 위해 각 병원의 의료 데이터를 통합하고 접근하기 위한 방법의 필요성이 증대되고 있는 상황이다. 이러한 요구사항을 만족시키기 위해 그리드 컴퓨팅 기술을 도입하여 고효율의 처리 능력과 풍부한 데이터 저장 공간을 제공하고자 하였으며, 원격의 사용자가 지역적 데이터에 접근할 수 있도록 하는 데이터 관리 서비스를 웹서비스로 제공하는 방법에 대해 제시하였다. 또한, 프로토타입을 설계, 구현하여 실제 가능성에 대해 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.71-85
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• 2006

The environment of computational grid is composed of the LAN/WAN each of which has different efficiency and heterogeneous network conditions, and where various programs are running. In this environment, the role of the resource selection broker is very important because the work of each node is performed by considering heterogeneous network environment and the computing power of each node according to the characteristics of a program. In this paper, a new resource selection broker is presented that decides the number of work processes to be allocated at each node by considering network state information and the performance of each node according to the characteristics of a program in the environment of computational grid. The proposed resource selection broker has three steps as follows. First, the performance ratio of each node is computed using latency-bandwidth-cpu mixture information reflecting the characteristics of a program, and the number of work processes that will be performed at each node are decided by this ratio. Second, RSL file is automatically made based on the number of work processes decided at the previous step. Finally, each node creates work processes by using that RSL file and performs the work which has been allocated to itself. As experimental results, the proposed method reflecting characteristics of a program, compared with the existing (uniformity) and latency-bandwidth method is improved $278%\sim316%,\;524%\sim595%,\;924%\sim954%$ in the point of work amount, work process number, and node number respectively.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.441-448
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• 2008

The molecular docking system needs a large amount of computation and requires super-computing power. Since the experiment requires a large amount of time, the experiment is conducted in the distributed environment or in the grid environment. Recently, researches on using parallel GPU of far higher performance than that of CPU in scientific computing have been very actively conducted. CUDA is an open technique by which a parallel GPU programming is made possible. This study proposes the molecular docking system using CUDA. It also proposes algorithm that parallels energy-minimizing-computation. To verify such experiments, this study conducted a comparative analysis on the time required for experimenting molecular docking in general CPU and the time and performance of the parallel GPU-based molecular docking which is proposed in this study.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.4
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• pp.1-33
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• 2019

The key to the long-term preservation of electronic records is the long-term retention of the bitstream that contains the original functional attributes and appearances of the electronic records that were originally created and utilized. Considering economic and technical aspects, most of the major domestic and foreign archives have adopted a migration strategy that allows conversion to a new format depending on the stability and reliability of the format containing the original bitstream. However, as the type of electronic records varies and its scope continuously expands, several problems arise if a single migration strategy is observed. Therefore, the applicability of the emulation strategy is examined in this paper to reproduce the system and application environment where electronic records are created and utilized. As such, electronic records can be maintained without any changes in the bitstream by utilizing cloud computing technology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1450-1458
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• 2011

The HPcN (Hybrid & high Performance Convergence Network) in research networks means environment which can provide both computing resource such as supercomputer, cluster and network resource to application researchers in the field of medical, bio, aerospace and e-science. The most representative research network in Korea, KREONET has been developing following technologies through the HERO (Hybrid Networking project for research oriented infrastructure) from 200S. First, we have constructed and deployed a control plane technology which can provide a connection oriented network dynamically. Second, the integrated resource management system technology has been developing for reservation and allocation of both computing and network resources, whenever users want to utilize them. In this paper, a testbed network is presented, which is possible to reserve and allocate network resource using the integrated resource management system. We reserve network resource through GNSI (Grid Network Service Interface) messages between GRS (Global Resource Scheduler) and NRM (Network Resource Manager) and allocate network resource through GUNI (Grid User Network Interface) messages between the NRM (network resource manager) and routers, based on reservation information provided from a user on the web portal. It is confirmed that GUNI interface messages are delivered from the NRM to each router at the starting of reservation time and traffic is transmitted through LSP allocated by the NRM.

• The KIPS Transactions:PartA
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• v.18A no.4
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• pp.123-128
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• 2011

As the global grid has grown in size, large-scale distributed data analysis schemes have gained momentum. Over the last few years, a number of methods have been introduced for allocating data intensive tasks across distributed and heterogeneous computing platforms. However, these approaches have a limited potential for scaling up computing nodes so that they can serve more tasks simultaneously. This paper tackles the scalability and communication delay for computing nodes. We propose a distributed data node for storing and allocating the data. This paper also provides data provisioning method based on the steady states for minimizing the communication delay between the data source and the computing nodes. The experimental results show that scalability and communication delay can be achieved in our system.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.157-172
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• 2011

The latest active studies on distributed OLAP to adopt a distributed environment are mainly focused on DHT P2P OLAP and Grid OLAP. However, these approaches have its weak points, the P2P OLAP has limitations to multidimensional range queries in the cloud computing environment due to the nature of structured P2P. On the other hand, the Grid OLAP has no regard for adjacency and time series. It focused on its own sub set lookup algorithm. To overcome the above limits, this paper proposes an efficient central managed P2P approach for a cloud computing environment. When a multi-level hybrid P2P method is combined with an index load distribution scheme, the performance of a multi-dimensional range query is enhanced. The proposed scheme makes the OLAP query results of a user to be able to reused by other users' volatile cube search. For this purpose, this paper examines the combination of an aggregation cube hierarchy tree, a quad-tree, and an interval-tree as an efficient index structure. As a result, the proposed cloud P2P OLAP scheme can manage the adjacency and time series factor of an OLAP query. The performance of the proposed scheme is analyzed by a series of experiments to identify its various characteristics.