The week's pick
Random Articles
Reseach Article
Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 170 - Number 6 |
| 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 ( Jul 2017), 39-42. DOI=10.5120/ijca2017914890
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.
References
- Herring, R. J. 2010. Real-time traffic modeling and estimation with streaming probe data using machine learning. Retrieved from http://search.proquest.com.ezp 01.library.qut.edu.au/docview/859247684?pq-origsite=summon.
- Herrera, J. C., Work, D. B., Herring, R., Ban, X., Jacobson, Q., and Baye, A. M. 2010. Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment. Transportation Research Part C, vol.18, issue 4, (2010), 568-583. doi:10.1016/j.trc.2009.10.006.
- How Scoot Works. Retrieved from http://www.scoot-utc.com/HowSCOOTWorks.php
- Garber, L. 2012. Using in-memory analytics to quickly crunch big data. Computer, vol. 45, issue 10, (2012),16-18. doi:10.1109/MC.2012.358.
- Obuhuma, J. I. and Moturi, C.A., 2012. Use of GPS with road mapping for traffic analysis. International Journal of Scientific and technology research.
- Cárdenas-Benítez, N., Aquino-Santos, R., Magaña-Espinoza, P., Aguilar-Velazco, J., Edwards-Block, A. and Medina Cass, A., 2016. Traffic congestion detection system through connected vehicles and big data. Sensors, 16(5), p.599.
- OECD, 2015. Big Data and Transport: Understanding and Assessing Options. Retrieved from http://www.internationaltransportforum.org.
- IBM, 2016. Traffic Management for a Smarter Planet. Retrieved from http://www.ibm.com/smarterplanet/us/en/ traffic_congestion/article/traffic-management-and-prediction.html.
- Hota, J. 2013. Adoption of In-Memory Analytics. CSI Communications, 20. Chicago. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2250411.
- Woo, B. 2014. Oracle’s ‘In-Memory’ option may be costly. Retrieved from http://www.neuralytix.com/blog/ 2014/06/11/oracles-in-memory-option-may-be-costly/
- Sheldon, R. 2015. Oracle in-memory database option: Intro and implementation. Retrieved from http://searchoracle.techtarget.com/tip/Oracle-in-memory-database-option-Intro-and-implementation.
- Haitao, C. 2013. Improving traffic management with big data analytics. Retrieved from http://www.intel.com.au/content/dam/www/public/us/en/documents/case-studies/big-data-xeon-e5-trustway-case-study.pdf.
- Karena, C. 2012. Top 6 big data issues. Retrieved from http://www.smh.com.au/it-pro/business-it/top-6-big-data-issues-20120816-24a6w.html#ixzz3km2jR0OC.
- Stelle, G., Olivier, S. L., Stark, D., Rodrigues, A. F. and Hemmert, K. S. 2014. Using a complementary emulation-simulation co-design approach to assess application readiness for processing-in-memory systems. (2014), 64-71. doi:10.1109/Co-HPC.2014.5.
Index Terms
Keywords