• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.401-408
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• 2014

Hadoop is composed with MapReduce programming model for distributed processing and HDFS distributed file system. Hadoop is suitable framework for big data processing, but processing of mass small files have many problems. The processing of mass small file in hadoop have problems to created one mapper per one file, and it have problems to needed many memory for store of meta information of file. This paper have comparison evaluation processing method of mass small file with various method in hadoop platform. The processing of general compression format is inadequate because of processing by one mapper regardless of data size. The processing of sequence and hadoop archive file is removed memory problem of namenode by compress and combine of small file. Hadoop archive file is faster then sequence file about combine time of small file. The processing using CombineFileInputFormat class is needed not combine of small file, and it have similar speed big data processing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.476-477
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• 2013

Gadget-2 is a scientific simulation code has been used for many different types of simulations like, Colliding Galaxies, Cluster Formation and the popular Millennium Simulation. The code is parallelized with Message Passing Interface (MPI) and is written in C language. There is also a Java adaptation of the original code written using MPJ Express called Java Gadget. Java Gadget writes a lot of checkpoint data which may or may not use the HDF-5 file format. Since, HDF-5 is MPI-IO compliant, we can use our MPJ-IO library to perform parallel reading and writing of the checkpoint files and improve I/O performance. Additionally, to add reliability to the code execution, we propose the usage of Hadoop Distributed File System (HDFS) for writing the intermediate (checkpoint files) and final data (output files). The current code writes and reads the input, output and checkpoint files sequentially which can easily become bottleneck for large scale simulations. In this paper, we propose Sim-Hadoop, a framework to leverage HDFS and MPJ-IO for improving the I/O performance of Java Gadget code.

• 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.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.181-186
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• 2014

Web data mining aims at discovering useful knowledge from various Web resources. There is a growing trend among companies, organizations, and individuals alike of gathering information through Web data mining to utilize that information in their best interest. In science, cloud computing is a synonym for distributed computing over a network; cloud computing relies on the sharing of resources to achieve coherence and economies of scale, similar to a utility over a network, and means the ability to run a program or application on many connected computers at the same time. In this paper, we propose a new system framework based on the Hadoop platform to realize the collection of useful information of Web resources. The system framework is based on the Map/Reduce programming model of cloud computing. We propose a new data mining algorithm to be used in this system framework. Finally, we prove the feasibility of this approach by simulation experiment.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1122-1133
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• 2019

We have designed and implemented a new data processing framework called MOHA(Mtc On HAdoop) which can effectively support Many-Task Computing(MTC) applications in a YARN-based Hadoop platform. MTC applications can be composed of a very large number of computational tasks ranging from hundreds of thousands to millions of tasks, and each MTC application may have different resource usage patterns. Therefore, we have implemented MOHA-TaskExecutor(a pilot-job that executes real MTC application tasks)'s Adaptive Parallel Computability which can adaptively execute multiple tasks simultaneously, in order to improve the parallel computability of a YARN container and the overall system throughput. We have implemented multi-threaded version of TaskExecutor which can "independently and dynamically" adjust the number of concurrently running tasks, and in order to find the optimal number of concurrent tasks, we have employed Hill-Climbing algorithm.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• 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 the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.803-813
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• 2013

As the developments of smart devices and social network services, the amount of data has been exploding. The world is facing Big data era. For these reasons, the Big data processing technology which is a new technology that can handle such data has attracted much attention. One of the most representative technologies is Hadoop. Hadoop Distributed File System(HDFS) designed to run on commercial Linux server is an open source framework and can store many terabytes of data. The initial version of Hadoop did not consider security because it only focused on efficient Big data processing. As the number of users rapidly increases, a lot of sensitive data including personal information were stored on HDFS. So Hadoop announced a new version that introduces Kerberos and token system in 2009. However, this system is vulnerable to the replay attack, impersonation attack and other attacks. In this paper, we analyze these vulnerabilities of HDFS security and propose a new protocol which complements these vulnerabilities and maintains the performance of Hadoop.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.253-258
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• 2014

Recently, the development of cloud computing technology is developed as soon as smartphones is increases, and increased that users want to receive big data service. Hadoop framework of the big data service is provided to hadoop file system and hadoop mapreduce supported by data-intensive distributed applications. But, smpartphone service using hadoop system is a very vulnerable state to data authentication. In this paper, we propose a initial authentication protocol of hadoop system assisted by smartphone service. Proposed protocol is combine symmetric key cryptography techniques with ECC algorithm in order to support the secure multiple data processing systems. In particular, the proposed protocol to access the system by the user Hadoop when processing data, the initial authentication key and the symmetric key instead of the elliptic curve by using the public key-based security is improved.

• Journal of IKEEE
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• v.21 no.2
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• pp.142-145
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• 2017

Currently, a trend in image processing is high-quality and high-resolution. The size and amount of image data are increasing exponentially because of the development of information and communication technology. Thus, license plate recognition with a single processor cannot handle the increasing data. This paper proposes a number plate recognition system using a distributed processing framework, Hadoop. Using SequenceFile format in Hadoop, each mapper performs a license plate recognition with a number of image data in a data block Experimental results show that license plate recognition performance with 16 data nodes accomplishes speedup of maximum 14.7 times comparing with one data node. In large dataset, the recognition performance is robust even if the number of data nodes increases gradually.