Continuous Distributed Top-k Monitoring over High-Speed Rail Data Stream in Cloud Computing Environment | Zendy

Hanning Wang | Zendy; Weixiang Xu | Zendy; Dongyan Xu | Zendy; Lili Wei | Zendy; Chaolong Jia | Zendy

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Continuous Distributed Top-k Monitoring over High-Speed Rail Data Stream in Cloud Computing Environment

Author(s) -

Hanning Wang,

Weixiang Xu,

Dongyan Xu,

Lili Wei,

Chaolong Jia

Publication year - 2013

Publication title -

advances in mechanical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.318

H-Index - 40

eISSN - 1687-8140

pISSN - 1687-8132

DOI - 10.1155/2013/590234

Subject(s) - cloud computing , focus (optics) , computer science , real time computing , continuous monitoring , monotone polygon , scale (ratio) , distributed computing , engineering , mathematics , physics , geometry , quantum mechanics , optics , operating system , operations management

In the environment of cloud computing, real-time mass data about high-speed rail which is based on the intense monitoring of large scale perceived equipment provides strong support for the safety and maintenance of high-speed rail. In this paper, we focus on the Top-k algorithm of continuous distribution based on Multisource distributed data stream for high-speed rail monitoring. Specifically, we formalized Top-k monitoring model of high-speed rail and proposed DTMR that is the Top-k monitoring algorithm with random, continuous, or strictly monotone aggregation functions. The DTMR was proved to be valid by lots of experiments

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