TY - GEN
T1 - Near real-Time big data analysis on vehicular networks
AU - Daniel, Alfred
AU - Paul, Anand
AU - Ahmad, Awais
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/5
Y1 - 2015/10/5
N2 - In this cutthroat era of 21st Century Traffic information is considered as one of the prominent valuable resources in vehicular networks for big data analysis. In order to effectively utilize the acquired resources, big data analysis in near real time will be an appropriate way to produce valuable information from raw data. In order to exhibit the importance of big data investigation, an efficient architecture has been proposed for near real time big data analysis in vehicular networks, which indeed will keep pace with the latest trends and development with respect to emerging big-data paradigm. The proposed architecture, comprises centralized data storage mechanism for batch processing and distributed data storage mechanism for streaming processing in real time analysis. Furthermore a work flow model has also been designed for big data architecture to examine streaming data in near real time process. Furthermore, an algorithm is designed for organizing the vehicle flow in a particular location or place. The proposed system model is for optimal utilization of the massive data set, meant for streaming data in near real time process intended for ITS (Intelligent Transportation System) in a vehicular environment.
AB - In this cutthroat era of 21st Century Traffic information is considered as one of the prominent valuable resources in vehicular networks for big data analysis. In order to effectively utilize the acquired resources, big data analysis in near real time will be an appropriate way to produce valuable information from raw data. In order to exhibit the importance of big data investigation, an efficient architecture has been proposed for near real time big data analysis in vehicular networks, which indeed will keep pace with the latest trends and development with respect to emerging big-data paradigm. The proposed architecture, comprises centralized data storage mechanism for batch processing and distributed data storage mechanism for streaming processing in real time analysis. Furthermore a work flow model has also been designed for big data architecture to examine streaming data in near real time process. Furthermore, an algorithm is designed for organizing the vehicle flow in a particular location or place. The proposed system model is for optimal utilization of the massive data set, meant for streaming data in near real time process intended for ITS (Intelligent Transportation System) in a vehicular environment.
KW - batch processing
KW - Centralized data storage
KW - distributed data storage
KW - ITS
KW - stream processing
UR - http://www.scopus.com/inward/record.url?scp=84973316058&partnerID=8YFLogxK
U2 - 10.1109/ICSNS.2015.7292404
DO - 10.1109/ICSNS.2015.7292404
M3 - Conference contribution
AN - SCOPUS:84973316058
T3 - Proceedings of the IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015
BT - Proceedings of the IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015
Y2 - 25 February 2015 through 27 February 2015
ER -