Open AccessContinuous 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/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