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基于云计算的数据挖掘平台架构及其关键技术研究 被引量:10

Research on data mining platform based on cloud computing and its key technologies
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摘要 信息时代初期,传统的数据挖掘技术解决了从复杂的信息中提取关键信息的问题。但随着时代不断发展前进,造成了数据信息恐怖的增长速度。云时代的来临,使传统的数据挖掘的瓶颈开始露出苗头。文章着重研究分析了基于云计算的数据挖掘平台的架构及其关键技术,最后笔者针对云计算数据挖掘平台的现状提出了若干建议。 Early in the information age, the traditional data mining technology has solved the problem ot ex- tracting the key information from the complex information. However, with the continuous development of the times,resulting in the extremely high growth rate of data information.At the advent of the cloud era, the bottleneck of traditional data mining began showing signs.In this paper,the architecture and key tech- nologies of data mining platform based on cloud computing are analyzed, and some suggestions are put for- ward.
作者 葛晓玢 刘杰
出处 《景德镇学院学报》 2017年第3期26-29,共4页 Journal of JingDeZhen University
基金 安徽省质量工程项目--省级特色专业计算机网络技术(2013tszy061) 铜陵职业技术学院教研科学研究项目--基于赛 学协同的高职网络专业实践教学改革探索(JY2016D006)
关键词 云计算 数据挖掘 平台架构 关键技术 建议 cloud computing data mining platform architecture key technology suggestion
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  • 1宋晓云,苏宏升.一种并行决策树学习方法研究[J].现代电子技术,2007,30(2):141-144. 被引量:4
  • 2HAN J W, KAMBER M, PEI J. Data mining: Concepts and techniques [M]. 3rd ed. San Francisco, CA, USA: Morgan Kaufmann Publishers, 2011.
  • 3LUO P. LU K, SHI Z Z, et al, Distributed data mining in grid computing environments [J]. Future Generation Computer Systems, 2007, 23(1 ):84-91.
  • 4LUO P, LU K, HUANG R, et al. A heterogeneous computing system for data mining workflows in mutti-agent environments [J]. Expert Systems, 2006,23(5):258-272.
  • 5ZHUANG F Z, HE Q, SHI Z Z, Multi-agent based on automatic evaluation system for classification algorithm [C]//Proceedings of the International Conference on Information and Automation(ICIA' 08),Jun 20-23,2008, Zhangjiajie, China. Piscataway, NJ, USA:IEEE 2008: 264-269.
  • 6HAMEENANTT(LA T, GUAN X L, CAROTHERS J D, et al. The flexible hypercube: A new fault-tolerant architecture for parallel computing [J]. Journal of Parallel and Distributed Computing, 1996,37(2): 213-220.
  • 7GOUDREAU M W, LANG K, RAO S B, et al. Portable and efficient parallel computing using the BSP model [J]. IEEE Transactions on Computers, 1999,48(7):670-689.
  • 8CHU CT, KIM S K, LIN YA, et al. Map-reduce for machine learning on multicore [C]//Proceedings of the 21 st Annual Conference on Neural Information Processing Systems (NIPS' 07), Dec 3-6,2007,Vancouver, Canada. Berlin, Germany: Springer-Verlag, 2007:281-288.
  • 9BORTHAKUR D. The hadoop distributed file system: Architecture and design [R], The Apache Software Foundation, 2007.
  • 10DEAN J, GHEMAWAT S. MapReduce: Simplified data processing on large clusters [J]. Communications of the ACM, 2008,51 (1): 107-113.

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