TY - GEN
T1 - Efficient On/Off-Line Query Pre-processing for Telecom Social Streaming Data
AU - Wu, Cheng
AU - Fu, Jigao
AU - Zhang, Zhen
AU - Liu, Chi Harold
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/11
Y1 - 2016/10/11
N2 - Social media are primarily generated and transmitted over Internet from mobile based applications/tools, e.g., Flickr, YouTube, etc., for sharing and discussing information among people. Most of these applications are putting forward from desktop to mobile client side, since smart devices are growing by leaps and bounds, and they tend to take fully advantage of tunnels that telecom companies offer. Like any other industries, telecom operators also face tough competitions from 'over-the-top'(OTT) service providers. Therefore, they are prone to bring in Big Data analytical techniques to take fully use of streaming data they possessed. To this end, in this paper, we propose a novel query system specifically designed for telecom networks that integrates both online pre-processing and offline analytics for social streaming data. Furthermore, our framework is able to speedup the query processing by creating and parsing an Abstract Syntax Tree (AST). Extensive experimental results show the effectiveness of proposed and implemented system.
AB - Social media are primarily generated and transmitted over Internet from mobile based applications/tools, e.g., Flickr, YouTube, etc., for sharing and discussing information among people. Most of these applications are putting forward from desktop to mobile client side, since smart devices are growing by leaps and bounds, and they tend to take fully advantage of tunnels that telecom companies offer. Like any other industries, telecom operators also face tough competitions from 'over-the-top'(OTT) service providers. Therefore, they are prone to bring in Big Data analytical techniques to take fully use of streaming data they possessed. To this end, in this paper, we propose a novel query system specifically designed for telecom networks that integrates both online pre-processing and offline analytics for social streaming data. Furthermore, our framework is able to speedup the query processing by creating and parsing an Abstract Syntax Tree (AST). Extensive experimental results show the effectiveness of proposed and implemented system.
KW - data streaming
UR - https://www.scopus.com/pages/publications/84995519488
U2 - 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.142
DO - 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.142
M3 - Conference contribution
AN - SCOPUS:84995519488
T3 - Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
SP - 827
EP - 834
BT - Proceedings - 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing, PICom 2016, 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
A2 - Wang, Kevin I-Kai
A2 - Jin, Qun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Zhang, Qingchen
A2 - Hsu, Ching-Hsien
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2016, 14th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2016, 2nd IEEE International Conference on Big Data Intelligence and Computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016
Y2 - 8 August 2016 through 10 August 2016
ER -