期刊文献+

大数据关键处理技术综述 被引量:28

Summary of Big Data Key Processing Technology
在线阅读 下载PDF
导出
摘要 大数据是继云计算、物联网之后IT产业又一次颠覆性的技术革命,大数据的发展、研究必将改变世界。先简介大数据的概念及其特征、大数据发展历程、大数据与云计算的关系;接着叙述了大数据分析和处理的比较成熟的平台:Spark和Hadoop;然后对大数据处理的若干关键技术:大数据采集、大数据预处理、大数据的存储及管理、大数据的分析和挖掘、大数据的统计分析等进行了较系统的分析、归纳和探讨。 Big data is a disruptive technological revolution,in IT field,after the cloud computing and EPC system network,and big data development and research will change the world.The conceptions and characteristics of big data,its development course,and the relationship between big data and cloud computing are introduced.Then the more mature platform,Spark and Hadoop of big data analysis and processing are described.And some key techniques for big data processing are systematically analyzed,summarized and discussed,such as big data acquisition,big data preprocessing,big data storage and management,big data analysis and mining,and statistical analysis of big data.
作者 杨刚 杨凯
出处 《计算机与数字工程》 2016年第4期694-699,共6页 Computer & Digital Engineering
基金 陕西省教育厅科学基金项目(15JK1134)资助
关键词 大数据 HADOOP 数据挖掘 NOSQL数据库 big data Hadoop data mining NoSQL database
  • 相关文献

参考文献14

二级参考文献366

  • 1刘俊.基于大数据流的Multi-Agent系统模型研究[J].计算机技术与发展,2007,17(5):166-169. 被引量:10
  • 2托夫勒.第三次浪潮[M].北京:中信出版社,2006.
  • 3Zhou MQ, Zhang R, Zeng DD, Qian WN, Zhou AY. Join optimization in the MapReduce environment for column-wise data store. In: Fang YF, Huang ZX, eds. Proc. of the SKG. Ningbo: IEEE Computer Society, 2010.97-104. [doi: 10.1109/SKG.2010.18].
  • 4Afrati FN, Ullman JD. Optimizing joins in a Map-Reduce environment. In: Manolescu I, Spaecapietra S, Teubner J, Kitsuregawa M, Leger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 99-110. [doi: 10.1145/ 1739041.1739056].
  • 5Sandholm T, Lai K. MapReduce optimization using regulated dynamic prioritization. In: Douceur JR, Greenberg AG, Bonald T, Nieh J, eds. Proc. of the SIGMETRICS. Seattle: ACM Press, 2009. 299-310. [doi: 10.1145/1555349.1555384].
  • 6Hoefler T, Lumsdaine A, Dongarra J. Towards; efficient MapReduce using MPI. In: Oster P, ed. Proc. of the EuroPVM/MPI. Berlin: Springer-Verlag, 2009. 240-249. [doi: 10.100'7/978-3-642-03770-2_30].
  • 7Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. PVLDB, 2010, 3(1-2):494-505.
  • 8Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in MapReduce. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 245-254. [doi: 10.1109/CLUSTER.2010.30].
  • 9Polo J, Carrera D, Becerra Y, Torres J, Ayguad6 E, Steinder M, Whalley I. Performance-Driven task co-scheduling for MapReduce environments. In: Tonouchi T, Kim MS, eds. Proc. of the 1EEE Network Operations and Management Symp. (NOMS). Osaka: IEEE Press, 2010. 373-380. [doi: 10.1109/NOMS.2010.5488494].
  • 10Zaharia M, Konwinski A, Joseph AD, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Draves R, van Renesse R, eds. Proc. of the ODSI. Berkeley: USENIX Association, 2008.29-42.

共引文献3470

同被引文献267

引证文献28

二级引证文献109

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部