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“大数据”背景下的信息处理技术分析与研究 被引量：3

A Research of Information Processing Technology for Big Data
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摘要 “大数据”是信息科技领域出现的一个研究焦点。文章从“大数据”的概念进入，对其特征进行梳理总结，对“大数据”处理的核心技术进行分析，比较分析各个“大数据”处理商业解决方案的特点，最后结合“大数据”特征分析科技文献信息，对“大数据”在科技文献信息处理领域的应用进行探讨性分析与研究。 Big Data is new research focused in the field of Information Science, which includes hardware and sottware. First, this paper introduces the concept of Big Data and summarizes the features of Big Data. Secondly, the paper focuses on Hadoop, the main technology of Big data. On the analysis of the advantages and shortages of Big Data, the paper compares and analyzes the typical services to resolve Big Data, which use Hadoop, cloud storage and cloud computing and traditional database technology. Finaly, the paper smnmarizcs the features of science and technology information and proposes the application of Big Data technology in the field of science and technology information service.
作者 于薇
机构地区 中国科学技术信息研究所
出处 《数字图书馆论坛》 2012年第11期6-11,共6页 Digital Library Forum
基金 中国科学技术信息研究所预研基金项目“基于数字资源的异构语义元数据融合技术及服务研究”（编号：YY201219）.
关键词 大数据 HADOOP 信息处理技术 Big data, Hadoop, Information processing technology, Science and technology information service
分类号 G276 [文化科学—档案学]
  • 相关文献

参考文献12

  • 1McKinsey Global Institute. Big data: The next frontier for innovation, competition, and productivity [OL]. [2012-08-21]. http://www.mckinsey. com/Insights/MGI/ Researeh/Technology_and_Innovation/Big_data The next frontier_forinnovation.
  • 2IBM. What is big data? [OL]. [2012-08-20]. http://www-01.ibm.com/software/data/bigdata/.
  • 3姜奇平.大数据时代到来[OL].(2012-02-02)[2012-08-15].http://www.ciweek.com/article/2012/0118/A20120118554491.shtml.
  • 4什么是大数据?[OL].(2012-09-07)[2012-09-18].http://www.enet.com.cn/article/2012/0907/A20120907159678.shtml.
  • 5Apache官方主页[DB/OL].http://Hadoop.apache.org/.
  • 6覃雄派,王会举,杜小勇,王珊.大数据分析——RDBMS与MapReduce的竞争与共生[J].软件学报,2012,23(1):32-45. 被引量：387
  • 7Hadoop [OL]. [2012-09-22]. http://baike.baidu.com/view/908354.htm.
  • 8PAVLO A, PAULSON E, RASIN A, et al. A comparison of approaches to large-scale data analysis [C]//SIGMOD '09: Proceedings of the 35th SIGMOD international conference on Management of data, New York, NY, USA, 2009: 165-178.
  • 9HDFS-273 [OL]. [2012-09-02]. https://issues.apache.org/jira/browse/HDFS-273.
  • 10[OL]. [2012-09-02]. http://www.searchdatabase.com.cn/showcontent_56707.htm.

二级参考文献82

  • 1Zhou 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].
  • 2Afrati 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].
  • 3Sandholm 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].
  • 4Hoefler 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].
  • 5Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. PVLDB, 2010, 3(1-2):494-505.
  • 6Kambatla 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].
  • 7Polo 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].
  • 8Zaharia 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.
  • 9Xie J, Yin S, Ruan XJ, Ding ZY, Tian Y, Majors J, Manzanares A, Qin X. Improving MapReduce performance through data placement in heterogeneous Hadoop clusters. In: Taufer M, Rfinger G, Du ZH, eds. Proc. of the Workshop on Heterogeneity in Computing (IPDPS 2010). Atlanta: IEEE Press, 2010. 1-9. [doi: 10.1109/IPDPSW.2010.5470880].
  • 10Polo J, Carrera D, Becerra Y, Beltran V, Torres J, Ayguad6 E. Performance management of accelerated MapReduce workloads in heterogeneous clusters. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 653-662. [doi: 10.1109/ ICPP.2010.73].

共引文献389

同被引文献18

引证文献3

二级引证文献31

数字图书馆论坛
2012年 第11期

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