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Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Aiman Abdul-Razzak Fatehi Al-Sabaawi
10.5120/ijca2017914890

Aiman Abdul-Razzak Fatehi Al-Sabaawi. Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages. International Journal of Computer Applications 170(6):39-42, July 2017. BibTeX

@article{10.5120/ijca2017914890,
	author = {Aiman Abdul-Razzak Fatehi Al-Sabaawi},
	title = {Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {6},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {39-42},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume170/number6/28078-2017914890},
	doi = {10.5120/ijca2017914890},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A method for loading detailed data from more than one source into the main memory in traffic system is presented. The challenges include data volume, data velocity, and Data variation of the traffic. The method uses In-memory analytics to improve query evaluation, to analysis and process reports in the traffic system. This occurs with the development of multicore processors, the loading of data, and the way image and video traffic data are stored and transferred into/from the data centers of different divisions and centralizing access to traffic management facilities, equipment and application systems.

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Keywords

Traffic Congestion, In-memory analytics, Real-time, Data volume, Data variety, Data velocity, Big data.