• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.28-38
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• 2014

In this paper, we propose the distributed cache management scheme to efficiently access small files in Hadoop Distributed File Systems(HDFS). The proposed scheme can reduce the number of metadata managed by a name node since many small files are merged and stored in a chunk. It is also possible to reduce the file access costs, by keeping the information of requested files using the client cache and data node caches. The client cache keeps small files that a user requests and metadata. Each data node cache keeps the small files that are frequently requested by users. It is shown through performance evaluation that the proposed scheme significantly reduces the processing time over the existing scheme.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.21-27
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• 2013

In recently years, with the growth of social media and the development of mobile devices, the data have been significantly increased. Hadoop has been widely utilized as a typical distributed storage and processing framework. The tasks in Mapreduce based on the Hadoop distributed file system are allocated to the map as close as possible by considering the data locality. However, there are data being requested frequently according to the data analysis tasks of Mapreduce. In this paper, we propose a hot-data replication mechanism to improve the processing speed of Mapreduce according to data access patterns. The proposed scheme reduces the task processing time and improves the data locality using the replica optimization algorithm on the high access frequency of hot data. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme in terms of the load of access frequency.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.32-43
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• 2013

These days, there are more and more IT research institutions which foresee cloud services as the predominant IT service in the near future and there, in fact, are actual cloud services provided by some IT leading vendors. Regardless of physical location of the service and environment of the system, cloud service can provide users with storage services, usage of data and software. On the other hand, cloud service has challenges as well. Even though cloud service has its edge in terms of the extent to which the IT resource can be freely utilized regardless of the confinement of hardware, the availability is another problem to be solved. Hence, this paper is dedicated to tackle the aforementioned issues; prerequisites of cloud computing for distributed file system, open source based Hadoop distributed file system, in-memory database technology and high availability database system. Also the author tries to body out the high availability mass distributed data management architecture in cloud service's perspective using currently used distributed file system in cloud computing market.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.4
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• pp.141-146
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• 2015

Data have been increased enormously due to the development of IT technology such as recent smart equipments, social network services and streaming services. To meet these environments the technologies that can treat mass data have received attention, and the typical one is Hadoop. Hadoop is on the basis of open source, and it has been designed to be used at general purpose computers on the basis of Linux. To initial Hadoop nearly no security was introduced, but as the number of users increased data that need security increased and there appeared new version that introduced Kerberos and Token system in 2009. But in this method there was a problem that only one secret key can be used and access permission to blocks cannot be authenticated to each user, and there were weak points that replay attack and spoofing attack were possible. Hence, to supplement these weak points and to maintain efficiency a protocol on the basis of group key, in which users are authenticated in logical group and then this is reflected to token, is proposed in this paper. The result shows that it has solved the weak points and there is no big overhead in terms of efficiency.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.1-6
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• 2016

Many studies have been carried out for the development of big data utilization and analysis technology recently. There is a tendency that government agencies and companies to introduce a Hadoop of a processing platform for analyzing big data is increasing gradually. Increased interest with respect to the processing and analysis of these big data collection technology of data has become a major issue in parallel to it. However, study of the collection technology as compared to the study of data analysis techniques, it is insignificant situation. Therefore, in this paper, to build on the Hadoop cluster is a big data analysis platform, through the Apache sqoop, stylized from relational databases, to collect the data. In addition, to provide a sensor through the Apache flume, a system to collect on the basis of the data file of the Web application, the non-structured data such as log files to stream. The collection of data through these convergence would be able to utilize as a basic material of big data analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.218-220
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• 2012

인터넷 활용과 웹 어플리케이션의 개발이 증가함에 따라 처리해야하는 데이터의 양도 또한 증가하고 있다. 대량의 데이터를 효과적으로 처리하기 위한 방법 중 하나로 병렬처리 프로그래밍 모델인 맵리듀스가 있다. 하둡은 맵리듀스의 오픈소스 구현으로 대량의 데이터를 병렬로 처리하는 무료 자바 소프트웨어 프레임워크이다. 분산 파일 시스템을 사용하는 하둡에서는 처리하는 데이터가 다른 노드에 위치하는 데이터 로컬리티 문제가 전체 작업 수행시간의 증가를 야기하는 문제가 있다. 본 논문에서는 하둡에서의 데이터 로컬리티 문제를 해결하기 위한 데이터 복제기법을 제안한다. 제안하는 데이터 복제기법에서는 1) 라그랑지 보간법을 사용하여 과거 접근수를 이용한 미래 접근수를 예측하고, 2) 예측된 값을 Threshold값으로 설정하고, 3) 데이터 로컬리티 문제가 발생하였을 때, 복제사본을 생성할 것인지 캐시를 생성할 것인지를 결정하여 복제 사본의 수를 최적화 한다. 실험을 통해 단순히 복제사본 수를 증가시킴으로써 데이터 로컬리티를 향상을 이루어도 작업 완료시간이 감소하는 것이 아니라는 결과를 볼 수 있었고, 오버 런치로 인한 작업 완료시간 증가를 줄이기 위해 데이터 복제사본 수 최적화의 필요성을 확인할 수 있었다.

• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.651-657
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• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.78-80
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• 2017

3D 프린터를 이용하여 출력을 하기 위해서는 3D 모델 데이터를 G-code로 변환하는 과정을 수행해야 한다. 일반적으로 3D 모델은 STL 파일 형식으로 저장되는데, 이 파일은 대개 삼각형 형식인 페이셋들의 좌표 데이터를 포함하고 있다. 만약 3D 모델의 크기가 커지거나 정밀도가 높아진다면, 페이셋의 수가 매우 많아지게 되고, 결과적으로 3D 모델에서 G-code로 변환하는 시간이 길어지게 된다. 본 논문에서는 널리 활용되고 있는 Hadoop에서 변환 소프트웨어를 개발하고자 하였다. Hadoop은 마스터 노드와 여러 데이터 노드들이 Map-Reduce 방식으로 작업을 수행한다. 이러한 노드들은 하둡 파일시스템(HDFS)을 공유할 수 있어 작업을 효율적으로 수행할 수 있다. 이에 본 논문에서는 이 시스템의 기능을 활용하여 기존에 개발된 분산 알고리즘을 변형한 후 이를 구현하고자 한다.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.975-987
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• 2013

The Hadoop system was developed by the Apache foundation based on GFS and MapReduce technologies of Google. Many modern systems for managing and processing the big data have been developing based on the Hadoop because the Hadoop was designed for scalability and distributed computing. The R software has been considered as a well-suited analytic tool in the Hadoop based systems because the R is flexible to other languages and has many libraries for complex analyses. We introduced Rhipe which is a R package supporting MapReduce programming easily under the Hadoop system, and implemented a MapReduce program using Rhipe for multiple regression especially. In addition, we compared the computing speeds of our program with the other packages (ff and bigmemory) for processing the large data. The simulation results showed that our program was more fast than ff and bigmemory as the size of data increases.