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首页> 外文期刊>Acoustic bulletin >A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems
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A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems

机译：使用粗糙集理论在不同MapReduce运行时系统上并行进行大规模知识获取的比较

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摘要

Nowadays, with the volume of data growing at an unprecedented rate, large-scale data mining and knowledge discovery have become a new challenge. Rough set theory for knowledge acquisition has been successfully applied in data mining. The recently introduced MapReduce technique has received much attention from both scientific community and industry for its applicability in big data analysis. To mine knowledge from big data, we present parallel large-scale rough set based methods for knowledge acquisition using MapReduce in this paper. We implemented them on several representative MapReduce runtime systems: Hadoop, Phoenix and Twister. Performance comparisons on these runtime systems are reported in this paper. The experimental results show that (1) The computational time is mostly minimum on Twister while employing the same cores; (2) Hadoop has the best speedup for larger data sets; (3) Phoenix has the best speedup for smaller data sets. The excellent speedups also demonstrate that the proposed parallel methods can effectively process very large data on different runtime systems. Pitfalls and advantages of these runtime systems are also illustrated through our experiments, which are helpful for users to decide which runtime system should be used in their applications.
机译：如今，随着数据量以前所未有的速度增长，大规模数据挖掘和知识发现已成为新的挑战。知识获取的粗糙集理论已经成功地应用于数据挖掘中。最近引入的MapReduce技术因其在大数据分析中的适用性而受到了科学界和业界的广泛关注。为了从大数据中挖掘知识，本文提出了基于并行大规模粗糙集的MapReduce知识获取方法。我们在几个代表性的MapReduce运行时系统上实现了它们：Hadoop，Phoenix和Twister。本文报告了这些运行时系统的性能比较。实验结果表明：（1）在使用相同内核的情况下，Twister的计算时间最短。 （2）Hadoop对于较大的数据集具有最佳的加速； （3）Phoenix对于较小的数据集具有最佳的加速效果。出色的加速效果还表明，所提出的并行方法可以在不同的运行时系统上有效地处理非常大的数据。这些运行时系统的陷阱和优势也通过我们的实验得到了说明，这有助于用户确定应在其应用程序中使用哪个运行时系统。

著录项

  • 来源
    《Acoustic bulletin》 |2014年第3期|896-907|共12页
  • 作者

    Junbo Zhang; Jian-Syuan Wong; Tianrui Li; Yi Pan;

  • 作者单位

    School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China,Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA;

    Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA;

    School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;

    Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Rough sets; Knowledge acquisition; MapReduce; Large-scale;

    机译：粗糙集;知识获取;MapReduce;大规模;

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