大数据在移动通信中的应用被引量：7

Big data applications in mobile communication

下载PDF

导出

摘要大数据技术背景下,移动通信网络中各种零星、分散的信息得到有效整合,充分而深入地挖掘移动通信潜力,成为开发移动通信更多服务和业务的平台。以移动通信的大数据应用为中心,分析了大数据应用于移动通信的关键技术——大数据分析技术、大数据存储技术,提出了移动通信领域应用大数据技术的措施与方法。 Under the background of big data technology background, a variety of mobile communication networks sporadic, scattered information effectively integrate fully and dig deeper potential of mobile communications, the development of mobile communications become more service and business platform. Large data applications for mobile communications center, a large data analysis of key technologies used in mobile communications-the big data analysis techniques and large data storage technology, mobile communications applications made big data technology measures and methods.

作者黄倩蓉

机构地区中国移动通信集团设计院有限公司黑龙江分公司

出处《黑龙江科学》 2015年第3期28-29,共2页 Heilongjiang Science

关键词大数据移动通信大数据分析技术大数据存储技术 Big data mobile communications big data analysis techniques large data storage technology

分类号 TN919 [电子电信—通信与信息系统]

引文网络
相关文献

参考文献5

1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：386
2从“大数据”到“大洞察”:迎接下一秒的价值和机遇[J].中国企业家,2012(12):145-145. 被引量：1
3张慷.电信行业WiFi商业模型及大客户管理平台[J].中国新通信,2014,16(13):1-2. 被引量：2
4张俊.移动通信网络中大数据处理的关键技术研究[J].电信网技术,2014(4):10-12. 被引量：17
5孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(1):146-169. 被引量：2391

二级参考文献257

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].