大数据时代的轨道交通公共安全体系研究被引量：4

Research on Public Safety System of Rail Transit in the Era of Big Data

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摘要公共安全连着千家万户,加强公共安全体系建设,确保公共安全事关人民群众生命财产安全,事关改革发展稳定大局。轨道交通作为承载城市运输的大动脉,其公共安全的保障工作在现代城市治理的统筹布局中始终占据着举足轻重的关键地位。本文研究了大数据背景下的城市轨道交通公共安全体系的建设背景、核心概念、发展现状和体系架构,分析了体系的三个主要组成部分—公共安全框架、风险治理模型和应急管理平台的核心内容,描述了建设公共安全体系需要的相关大数据技术。 Public safety is crucial for the numerous families. Strengthen the construction of public safety system to ensure public safety is vital for people＇s life and property safety and the social＇s overall situation of reform, development and stability. As a large artery carrying urban transportation, safety of rail transit plays an important role in the modern city governance. This paper studies the background, key concepts, development status and system architecture of the public safety system of urban rail transit with regard to the background of big data, analyzes the three main components of the system： public safety framework, risk governance model and emergency management platform, and describes the relevant big data technology to build public safety system.

作者孔磊

机构地区中共上海市委党校第三分校

出处《软件产业与工程》 2016年第1期49-52,56,共5页

关键词轨道交通公共安全大数据 Rail Transit, Public Safety, Big Data

分类号 TP393.08 [自动化与计算机技术—计算机应用技术]

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1孔磊.城市轨道交通公共安全大数据共享资源库研究[J].交通与港航,2015,2(5):37-41. 被引量：4
2李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J].中国科学院院刊,2012,27(6):647-657. 被引量：1591
3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

二级参考文献101

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].