• Title/Summary/Keyword: RHadoop

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Usefulness of RHadoop in Case of Healthcare Big Data Analysis (RHadoop을 이용한 보건의료 빅데이터 분석의 유효성)

  • Ryu, Wooseok
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2017.10a
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    • pp.115-117
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    • 2017
  • R has become a popular analytics platform as it provides powerful analytic functions as well as visualizations. However, it has a weakness in which scalability is limited. As an alternative, the RHadoop package facilitates distributed processing of R programs under the Hadoop platform. This paper investigates usefulness of the RHadoop package when analyzing healthcare big data that is widely open in the internet space. To do this, this paper has compared analytic performances of R and RHadoop using the medical treatment records of year 2015 provided by National Health Insurance Service. The result shows that RHadoop effectively enhances processing performance of healthcare big data compared with R.

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Performance Evaluation of Medical Big Data Analysis based on RHadoop (RHadoop 기반 보건의료 빅데이터 분석의 성능 평가)

  • Ryu, Woo-Seok
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.1
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    • pp.207-212
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    • 2018
  • As a data analysis tool which is becoming popular in the Big Data era, R is rapidly expanding its user range by providing powerful statistical analysis and data visualization functions. Major advantage of R is its functional scalability based on open source, but its scale scalability is limited, resulting in performance degrades in large data processing. RHadoop, one of the extension packages to complement it, can improve data analysis performance as it supports Hadoop platform-based distributed processing of programs written in R. In this paper, we evaluate the validity of RHadoop by evaluating the performance improvement of RHadoop in real medical big data analysis. Performance evaluation of the analysis of the medical history information, which is provided by National Health Insurance Service, using R and RHadoop shows that RHadoop cluster composed of 8 data nodes can improve performance up to 8 times compared with R.

RHadoop platform for K-Means clustering of big data (빅데이터 K-평균 클러스터링을 위한 RHadoop 플랫폼)

  • Shin, Ji Eun;Oh, Yoon Sik;Lim, Dong Hoon
    • 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.

Big data distributed processing system using RHadoop (RHadoop을 이용한 빅데이터 분산처리 시스템)

  • Shin, Ji Eun;Jung, Byung Ho;Lim, Dong Hoon
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.5
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    • pp.1155-1166
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    • 2015
  • It is almost impossible to store or analyze big data increasing exponentially with traditional technologies, so Hadoop is a new technology to make that possible. In recent R is using as an engine for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with various data sizes of actual data and simulated data. Experimental results showed our RHadoop system was faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and biglm packages available on bigmemory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Comparison analysis of big data integration models (빅데이터 통합모형 비교분석)

  • Jung, Byung Ho;Lim, Dong Hoon
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.4
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    • pp.755-768
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    • 2017
  • As Big Data becomes the core of the fourth industrial revolution, big data-based processing and analysis capabilities are expected to influence the company's future competitiveness. Comparative studies of RHadoop and RHIPE that integrate R and Hadoop environment, have not been discussed by many researchers although RHadoop and RHIPE have been discussed separately. In this paper, we constructed big data platforms such as RHadoop and RHIPE applicable to large scale data and implemented the machine learning algorithms such as multiple regression and logistic regression based on MapReduce framework. We conducted a study on performance and scalability with those implementations for various sample sizes of actual data and simulated data. The experiments demonstrated that our RHadoop and RHIPE can scale well and efficiently process large data sets on commodity hardware. We showed RHIPE is faster than RHadoop in almost all the data generally.

Performance Comparison of Logistic Regression Algorithms on RHadoop

  • Jung, Byung Ho;Lim, Dong Hoon
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.4
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    • pp.9-16
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    • 2017
  • Machine learning has found widespread implementations and applications in many different domains in our life. Logistic regression is a type of classification in machine leaning, and is used widely in many fields, including medicine, economics, marketing and social sciences. In this paper, we present the MapReduce implementation of three existing algorithms, this is, Gradient Descent algorithm, Cost Minimization algorithm and Newton-Raphson algorithm, for logistic regression on RHadoop that integrates R and Hadoop environment applicable to large scale data. We compare the performance of these algorithms for estimation of logistic regression coefficients with real and simulated data sets. We also compare the performance of our RHadoop and RHIPE platforms. The performance experiments showed that our Newton-Raphson algorithm when compared to Gradient Descent and Cost Minimization algorithms appeared to be better to all data tested, also showed that our RHadoop was better than RHIPE in real data, and was opposite in simulated data.

Research on Big Data Integration Method

  • Kim, Jee-Hyun;Cho, Young-Im
    • 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.

Big Data Platform Based on Hadoop and Application to Weight Estimation of FPSO Topside

  • Kim, Seong-Hoon;Roh, Myung-Il;Kim, Ki-Su;Oh, Min-Jae
    • 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.

Adaptive Frequent Pattern Algorithm using CAWFP-Tree based on RHadoop Platform (RHadoop 플랫폼기반 CAWFP-Tree를 이용한 적응 빈발 패턴 알고리즘)

  • Park, In-Kyu
    • Journal of Digital Convergence
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    • v.15 no.6
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    • pp.229-236
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    • 2017
  • An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

A Study on the Platform for Big Data Analysis of Manufacturing Process (제조 공정 빅데이터 분석을 위한 플랫폼 연구)

  • Ku, Jin-Hee
    • Journal of Convergence for Information Technology
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    • v.7 no.5
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    • pp.177-182
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    • 2017
  • As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.