Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80956
Title: Towards SDN-Enabled Big Data Platform for Social TV Analytics
Authors: Hu, Han
Wen, Yonggang
Gao, Yue
Chua, Tat-Seng
Li, Xuelong
Keywords: Big Data
SDN
Hadoop
Social TV Analytics
Issue Date: 2015
Source: Hu, H., Wen, Y., Gao, Y., Chua, T.-S., & Li, X. (2015). Toward an SDN-enabled big data platform for social TV analytics. IEEE Network, 29(5), 43-49.
Series/Report no.: IEEE Network
Abstract: The TV experience is being transformed with online social networks. TV audiences are sharing their opinions (i.e., social response) about video programs on OSNs (e.g., Twitter and Sina Weibo), thus providing a great opportunity for mining these data for stakeholders in TV value chains. This new paradigm is touted as social TV analytics, integrating the emerging big data research into TV. In this article, we envision and develop a unified big data platform for social TV analytics, extracting valuable insights from TV social response in a real-time manner. Such a platform presents tremendous challenges in networking architecture for our big data platform. We propose to build a cloud-centric platform with SDN support, providing on-demand virtual machines and reconfigurable networks. The architecture of our system consists of three key components, including a robust data crawler system, an SDN-enabled big data processing system, and a social media analytics system. The data crawler system adopts a distributed architecture to circumvent the access constraints of OSNs to crawl sufficient data about each TV program of interest; the SDN-enabled big data processing system integrates SDN and Hadoop, and exploits the SDN benefit to transfer intermediate data between different processing units to accelerate the data processing rate; and the social media analytics system extracts the public perception and knowledge related to TV programs based on microblog data. We have built a proof-of-concept demo over a private cloud at Nanyang Technological University. Feature verification and performance comparisons demonstrate the feasibility and effectiveness of the system.
URI: https://hdl.handle.net/10356/80956
http://hdl.handle.net/10220/38974
ISSN: 0890-8044
DOI: 10.1109/MNET.2015.7293304
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/MNET.2015.7293304].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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