• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.385-391
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• 2013

Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1118-1120
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• 2012

컴퓨터 메모리의 용량이 커지고 기술이 발전하며 메모리와 저장장치의 데이터 처리속도 차이는 나날이 커지고 있다. 이를 보완하고자 데이터 처리를 가급적 메모리에서 해결하여 처리속도를 높이고자 하는 연구가 많이 있다. 그 중 MapReduce에 대한 연구는 현재 주목이 되고 있는 분야이다. MapReduce는 빅데이터를 클러스터 환경에서 처리하기에 대중적인 프로그래밍 모델이다. 본 논문은 MapReduce 기반의 Hadoop을 SSD를 적용하여 실행속도를 증진시키려 한다. 전통적인 MapReduce 모델은 데이터를 정렬하는데에 I/O가 크게 발생하는데, MapRedce가 사용하는 병합정렬의 I/O 병목현상을 개선하고자 SSD를 사용하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.586-590
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• 2009

The objective of this paper describes the design of MapReduce over Peer-to-Peer network for dynamic environments applications. MapReduce is a software framework used for Cloud Computing which processing large data sets in a highly-parallel way. Based on the Peer-to-Peer network character which node failures will happen anytime, we focus on using a DHT routing protocol which named Pastry to handle the problem of node failures. Our results are very promising and indicate that the framework could have a wide application in P2P network systems while maintaining good computational efficiency and scalability. We believe that, P2P networks and parallel computing emerge as very hot research and development topics in industry and academia for many years to come.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.231-242
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• 2016

In order to meet rapid changes of Web information, it is necessary to extend the current Web technologies to represent both the valid time and location of each fact and knowledge, and reason their relationships. Until recently, many researches on qualitative temporal reasoning have been conducted in laboratory-scale, dealing with small knowledge bases. However, in this paper, we propose the design and implementation of a parallel qualitative temporal reasoner, MRQUTER, which can make reasoning over Web-scale large knowledge bases. This parallel temporal reasoner was built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs such as the encoding and decoding job, the inverse and equal reasoning job, the transitive reasoning job, the refining job, and applies some optimization techniques into each component reasoning job implemented with a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.573-579
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• 2011

To meet the scalability and performance requirements of data analyses, which often involve voluminous data, efficient parallel or concurrent algorithms and frameworks are essential. We present a high-performance Korean morphological analyzer which employs the MapReduce framework on the graphics processing unit (GPU). MapReduce is a programming framework introduced by Google to aid the development of web search applications on a large number of central processing units (CPUs). GPUs are designed as a special-purpose co-processor. Their programming interfaces are typically formulated for graphics applications. Compared to CPUs, GPUs have greater computation power and memory bandwidth; however, GPUs are more difficult to program because of the design of their architectures. The performance of the Korean morphological analyzer using the MapReduce framework on the GPU is evaluated in comparison with the CPU-based model. The proposed Korean Morphological analyzer shows promising scalable performance on distributed computing with the GPU.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.317-318
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• 2010

클라우드 컴퓨팅 서비스를 제공하기 위해서는 클라우드 컴퓨팅에 적합한 데이터 분산 저장 및 병렬 처리가 가능한 IT 인프라 구축이 필수적이다. 이를 위해서 분산 파일 시스템 중 하나인 HDFS(Hadoop File System)와 병렬 데이터 처리를 지원하기 위한 MapReduce 프레임워크 관련 연구가 각광 받고 있다. 하지만 MapReduce 프레임워크를 구성하는 JobTracker 노드는 SPoF(Single Point of Failure)이기 때문에, 작업 도중 JobTracker 노드의 결함이 발생하게 되면 전체 작업이 실패하게 된다. 위와 같은 문제를 해결하기 위해서 본 논문에서는 MapReduce 프레임워크의 JobTracker 노드 결함 발생에 대처할 수 있는 결함허용 메커니즘을 제안하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.121-122
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• 2009

인터넷 상에서 정보량이 급격히 증가함에 따라 ISP들은 데이터를 효과적으로 처리하고 분석하기 위한 방법을 연구하고 있다. 대표적으로 Google에서는 대용량의 분산 데이터 처리 기법인 MapReduce 모델을 개발하였다. 본 논문에서는 기존 MapReduce 모델에 Pipeline 방식을 적용하여 성능을 개선한 Pipeline-MapReduce 기법을 제안한다. 그리고 실험을 통해 제안 기법이 기존 기법에 비해 빠른 처리 결과를 나타냄을 보여준다.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.561-566
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• 2015

Hadoop is becoming widely adopted in scientific and commercial areas as an open-source distributed data processing framework. Recently, for real-time processing and analysis of data, an attempt to apply high-performance computing technologies to Hadoop is being made. In this paper, we have expanded the Hadoop Filesystem library to support Lustre, which is a popular high-performance parallel distributed filesystem, and implemented the Hadoop MapReduce execution environment over the Lustre filesystem. We analysed Hadoop MapReduce over Lustre by using Hadoop standard benchmark tools. We found that Hadoop MapReduce over Lustre execution has a performance 2-13 times better than a typical Hadoop MapReduce execution.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.668-676
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• 2017

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.609-619
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• 2016

RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. In this paper, we implement K-Means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. The main idea introduces a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. We showed that our K-Means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. We also implemented Elbow method with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset. Comparison with our MapReduce implementation of Elbow method and classical kmeans() in R with small data showed similar results.