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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.
Hu et al. (Tue,) studied this question.
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