• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.49-56
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• 2017

In this paper we propose the approach for big data integration so as to analyze, visualize and predict the future of the trend of the market, and that is to get the integration data model using the R language which is the future of the statistics and the Hadoop which is a parallel processing for the data. As four approaching methods using R and Hadoop, ff package in R, R and Streaming as Hadoop utility, and Rhipe and RHadoop as R and Hadoop interface packages are used, and the strength and weakness of four methods are described and analyzed, so Rhipe and RHadoop are proposed as a complete set of data integration model. The integration of R, which is popular for processing statistical algorithm and Hadoop contains Distributed File System and resource management platform and can implement the MapReduce programming model gives us a new environment where in R code can be written and deployed in Hadoop without any data movement. This model allows us to predictive analysis with high performance and deep understand over the big data.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.305-306
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• 2016

현재 스마트 시대에 살고 있는 우리는 매우 복잡하고 거미줄처럼 연결되어 있는 빅 데이터 환경에서 살고 있다. 이런 환경에서는 대용량 데이터를 효율적으로 관리하고 활용하는 것이 개인이나 기업들이 추구하려는 목표이다. 빅 데이터 시대에 데이터의 효율적인 관리와 활용을 위해 다양한 장비에서 수집되고 저장된 대용량 데이터에 대해서 일반적인 데이터 분석을 통한 보안 기술로는 상당한 시간과 자원 낭비가 수반된다. 이를 개선하기 위해 본 논문에서는 하둡을 이용하여 대용량 데이터에 대한 처리 및 분석을 통해 효과적인 보안 시스템을 제안한다.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.1
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• pp.32-40
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• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1785-1792
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• 2017

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.45-48
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• 2013

In this paper, we proposed an Indexing Structure System (ISS) based on Apache Hadoop in Wireless Sensor Network (WSN). Nowadays sensors data continuously keep growing that need to control. Data constantly update in order to provide the newest information to users. While data keep growing, data retrieving and storing are face some challenges. So by using the ISS, we can maximize processing quality and minimize data retrieving time. In order to design ISS, Indexing Types have to be defined depend on each sensor type. After identifying, each sensor goes through the Indexing Structure Processing (ISP) in order to be indexed. After ISP, indexed data are streaming and storing in Hadoop Distributed File System (HDFS) across a number of separate machines. Indexed data are split and run by MapReduce tasks. Data are sorted and grouped depend on sensor data object categories. Thus, while users send the requests, all the queries will be filter from sensor data object and managing the task by MapReduce processing framework.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.15-17
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• 2013

HDFS(Hadoop Distribute File System)는 대용량 파일 처리를 목적으로 설계 되었으며 현재 이상적인 분산 파일 시스템으로 각광 받고 있다. 이러한 HDFS는 기존 분산파일 시스템과 많은 유사성을 가지고 있으나, Fault Tolerance를 제공하고, 데이터 엑세스 패턴을 스트리밍 방식으로 지원하여 대용량 파일을 효율적으로 저장할 수 있다는 차별성을 가지고 있다. 하지만 실제 HDFS 데이터 집합에는 Small file이 차지하는 비중이 상당히 높으며, 이러한 다수의 Small file 은 데이터 처리에 있어 높은 비용을 초래할 뿐 아니라 Master Node 의 파일 처리 및 메모리 성능에 악영향을 미친다. 따라서 본 논문에서는 HDFS에서 Small file 이 미치는 영향을 분석하고 이러한 문제점을 해결 할 수 있는 로컬 인덱스 파일기반의 파일 병합 기법을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.1-6
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• 2015

The MapReduce Program of Hadoop Distributed File System operates on any unspecified nodes due to distributed-parallel process and block replicate for data stability. Since it is difficult to guarantee the cache locality when a Solid State Drive is used as a cache in hadoop, cache hit-rate is decreased. In this paper, we suggest a method to improve cache hit rate by pre-loading the input data of the MapReduce onto the SSD cache. To perform this method, we estimated the blocks that are used on each node by using capacity scheduler and block metadata. Eventually we could increase the performance of SSD cache by loading the blocks onto SSD cache before the Map Task run.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.188-191
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• 2013

인간과 사물, 서비스 세 가지 분산된 환경 요소에 대해 인간의 명시적 개입 없이 상호 협력적으로 센싱, 네트워킹, 정보 처리 등 지능적 관계를 형성하는 사물 공간 연결망인 IoT(Internet of Things)에서 센싱된 정보를 처리하고 서비스하기 위한 환경을 적시적소에 배치(Depolyment) 하기 위하여 클라우드 서비스와의 연동방법에 대해 본 논문에서 연구하였다. Public Cloud환경에서 Hadoop Cluster를 구성하여 IoT 서비스에 적용할 수 있는 통합 환경을 구축하면 폭발적으로 증가하는 IoT 데이터를 저장하고 빠른 시간안에 이를 효과적으로 처리 및 분석하기 위한 시스템 구축이 가능하며 분산 저장소에 저장된 데이터를 분석하고 의미있는 지식을 발견하여 새로운 비즈니스 모델 창출에 기여할 수 있다. 본 논문에서 Public Cloud 환경에서 Hadoop Clouster를 구성하여 IoT에서 생성되는 데이터를 효과적으로 처리하고 분석할 수 있는 방법을 제안한다.