期刊文献+

北京市实时浮动车系统架构改造实践研究

A Study on Transformation Practice of Real-Time Probe Vehicle System Architecture in Beijing
下载PDF
导出
摘要 随着物联网的发展,海量传感数据的涌入,关系型数据库日益出现功能、性能和稳定性上的瓶颈问题,业界领先的IT公司纷纷开始抛弃单关系型数据库的技术架构,交通运输行业信息系统对处理与存储数据量激增的现有系统进行软硬件架构升级改造与迁移也已是当务之急。本文以北京市实时浮动车系统的升级改造工程为例,阐述了城市交通核心业务系统的传统架构面临的实时计算与存储管理困境、新架构的解决方案,以及升级改造后的提升效果。 With the development of the Internet of Things, the influx of mass sensed data, and the bottleneck of relational databasesin functions, performance and stability , the industry, s leading IT companies have started to abandon the technical architecture of single relational databases, md the upgrade, Ixansfomation and migration of tlie software and hardware architectore of existing systems with a soaring size data for processing and storage in the information systems of the transportation industry have also become a priority. Taking the upgrade and transformation project for the real-time probe vehicle system of Beijing for instance, this paper describes the real-time computing and storage management dilemma in the traditional architecture of the urban transportation core business system, n e w architecture solutions, and the effect of enhancement after upgrade and transformation.
出处 《信息技术与信息化》 2016年第7期22-25,共4页 Information Technology and Informatization
基金 国家自然科学基金资助项目(No.61572069) 国家自然科学基金青年基金(No.71501014)
关键词 浮动车 大数据 HADOOP STORM 开源GIS 升级改造 Probe vehicle big data Hadoop Storm open-source GIS upgrade and reconstruction
  • 相关文献

参考文献5

二级参考文献27

  • 1DeWitt DJ, Paulson E, Robinson E, Naughton J. Clustera: An integrated computation and data management system. Proc. of the VLDB Endowment, 2008,1 (1):28-41. 被引量:1
  • 2Isard M, Prabhakaran V, Currey J, Wieder U. Quincy: Fair scheduling for distributed computing clusters. Proc. of the ACM SIGOPS 22nd symposium on operating systems principles. New York: ACM, 2009: 261-276. 被引量:1
  • 3Park SM, Humphrey M. Predictable Time-Sharing for DryadLINQ Cluster. Proc. of the 7th International Conference on Autonomic Computing, New York: ACM, 175-184. 被引量:1
  • 4Henzinger TA, Singh V, Wies T, Zufferey D. Scheduling Large Jobs by Abstraction Refinement. Proc. of the Sixth Conference on Computer Systems. New York: ACM, 2011: 329-342. 被引量:1
  • 5Kwon YC, Balazinska M, Howe B, Rolia J. Skew-Resistant Parallel Processing of Feature-Extracting Scientific User- Defined Functions. Proc.of the 1st ACM Symposium on Cloud Computing. New York: ACM, 2010: 75-86. 被引量:1
  • 6Ananthanarayanan G, Agarwal S, Kandula S, Greenberg A. Scarlett: Coping with Skewed Content Popularity in Map Reduce Clusters. Proc. of the Sixth Conference on Computer Systems. New York: ACM, 2011: 287-300. 被引量:1
  • 7Ko SY, Hoque I, Cho B, Gupta I. Making Cloud Intermediate Data Fault-Tolerant. Proc. of the 1st ACM Symposium on Cloud Computing. New York: ACM, 2010: 181-192. 被引量:1
  • 8Leo S, Zanetti Ct Pydoop: a Python MapReduce and HDFS API for Hadoop. Proc. of the 19th ACM International Symposium on High Performance Distributed Computing. New York: ACM, 2010. 被引量:1
  • 9Kim SG, Han H, Jung H, Eom H. Harnessing InputRedundancy in a MapReduce Framework. Proc. of the 2010 ACM Symposium on Applied Computing. New York: ACM, 2010: 362-366. 被引量:1
  • 10Ananthanarayanan G, Kandula S, Greenberg A, Stoica I. Reining in the Outliers in Map-Reduce Clusters using Mantri. Proc. of the 9th USENIX conference on Operating systems design and implementation. Berkeley: USENIX Association, 2010. 被引量:1

共引文献137

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