Statistical Analysis of the Massive Traffic Data Based on Cloud Platform

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Currently, with the rapid development of various geographic data acquisition technologies, the data-intensive geographic calculation is becoming more and more important. The urban motor vehicles loaded with GPS, namely the transport vehicles, can real-timely collect a large number of urban traffic information. If these massive transportation vehicle data can be real-timely collected and analyzed, the real-time and accurate basic information will be provided for monitoring the large area of traffic status as well as the intelligent traffic management. Based on the requirements of the organization, the processing, the statistics and the analysis of the massive urban traffic data, the new framework of the massive data-intensive calculation under the environment of cloud platform has been proposed through employing Bigtable, Mapreduce and other technologies.

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662-666

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July 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] E. Walker, 2008, Benchmarking Amazon EC2 for High-performance Scientific Computing, Login 33(5), pp.18-23.

Google Scholar

[2] F. Chang, J. Dean, S. Ghemawat, W. Hsieh, D. Wallach, M. Burrows, T. Charndra, A. Fike, and R. Gruber, 2006, Bigtable: A distributed storage system for structured data.Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2006).

DOI: 10.1145/1365815.1365816

Google Scholar

[3] L. Youseff, R. Wolski, B. Gorda and C. Krintz, 2006, Evaluating the Performance Impact of Xen on MPI and Process Execution for HPC Systems. In Proceedings of First International Workshop on Virtualization Technology in Distributed Computing, Tempa, Florida.

DOI: 10.1109/vtdc.2006.4

Google Scholar

[4] I. Gorton, P. Greenfield, A. Szalay and R. Willams, 2008,. Data-intensive computing in the 21st century. Computer, 41(4), pp.30-32.

DOI: 10.1109/mc.2008.122

Google Scholar

[5] X. Qiu, J. Enkanayake, S. Beason, T. Gunarathne, G. Fox, R. Barga and D. Gannon, 2009, Cloud technologies for bioinformatics application. In Proceedings of the 2nd Workshop on Many-task Computing on Grids and Supercomputers. Portland, Oregon.

DOI: 10.1145/1646468.1646474

Google Scholar