• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.107-114
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• 2020

This paper studies a method for estimating the scale of a Hadoop cluster to process big data as a cost-effective manner. In the case of medical institutions, demands for cloud-based big data analysis are increasing as medical records can be stored outside the hospital. This paper first analyze the Amazon EMR framework, which is one of the popular cloud-based big data framework. Then, this paper presents a efficiency model for scaling the Hadoop cluster to execute a Mapreduce application more cost-effectively. This paper also analyzes the factors that influence the execution of the Mapreduce application by performing several experiments under various conditions. The cost efficiency of the analysis of the big data can be increased by setting the scale of cluster with the most efficient processing time compared to the operational cost.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1731-1737
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• 2017

K Nearest Neighbor Join (KNN Join) is a simple yet effective method in machine learning. It is widely used in small dataset of the past time. As the number of data increases, it is infeasible to run this model on an actual application by a single machine due to memory and time restrictions. Nowadays a popular batch process model called MapReduce which can run on a cluster with a large number of computers is widely used for large-scale data processing. Hadoop is a framework to implement MapReduce, but its performance can be further improved by a new framework named Spark. In the present study, we will provide a KNN Join implement based on Spark. With the advantage of its in-memory calculation capability, it will be faster and more effective than Hadoop. In our experiments, we study the influence of different factors on running time and demonstrate robustness and efficiency of our approach.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.365-371
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• 2014

In the era of big data, algorithms for the existing IT environment cannot accept on a distributed architecture such as hadoop. Thus, new distributed algorithms which apply a distributed framework such as MapReduce are needed. Lloyd's algorithm commonly used for vector quantization is developed using MapReduce recently. In this paper, we proposed a decombined distributed VQ codebook generation algorithm based on a distributed VQ codebook generation algorithm using MapReduce to get a result more fast. The result of applying the proposed algorithm to big data showed higher performance than the conventional method.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.313-318
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• 2016

To utilize sensory data, it is necessary to design architecture for processing and handling data generated from sensors in an IoT environment. Especially in the IoT environment, a thing connects to the Internet and efficiently enables to communicate a device with diverse sensors. But Hadoop and Twister based on MapReduce are good at handling data in a batch processing. It has a limitation for processing stream data from a sensor in a motion. Traditional streaming data processing has been mainly applied a MoM based message queuing system. It has maintainability and scalability problems because a programmer should consider details related with complex messaging flow. In this paper architecture is designed to handle sensory data aggregated The designed software architecture is used to operate an application on the open source framework Storm. The application is conceptually used to transform streaming data which aggregated via sensor gateway by pipe-filter style.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.231-241
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• 2019

Bulk-loading an R-tree can be a good approach to build an efficient one. However, it takes a lot of time to bulk-load an R-tree for huge amount of data. In this paper, we propose a parallel R-tree construction scheme based on a Hadoop framework. The proposed scheme divides the data set into a number of partitions for which local R-trees are built in parallel via Map-Reduce operations. Then the local R-trees are merged into an global R-tree that covers the whole data set. While generating the partitions, it considers the spatial distribution of the data into account so that each partition has nearly equal amounts of data. Therefore, the proposed scheme gives an efficient index structure while reducing the construction time. Experimental tests show that the proposed scheme builds an R-tree more efficiently than the existing approaches.

• 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 Korean Data and Information Science Society
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• v.25 no.6
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• pp.1431-1438
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• 2014

We analyzed the articles from "Kukje Shinmun" and "Busan Ilbo", which are two local newpapers of Busan Metropolitan City. The articles cover from January 1, 2013 to December 31, 2013. Meaningful pattern inherent in 2889 articles of which the title includes "Busan" and "Traffic" and related data was analyzed. Textmining method, which is a part of datamining, was used for the social network analysis (SNA). HDFS and MapReduce (from Hadoop ecosystem), which is open-source framework based on JAVA, were used with Linux environment (Uubntu-12.04LTS) for the construction of unstructured data and the storage, process and the analysis of big data. We implemented new algorithm that shows better visualization compared with the default one from R package, by providing the color and thickness based on the weight from each node and line connecting the nodes.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.