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ViSTA-TV - Video Stream Analytics for Viewers in the TV Industry

Project description

SME initiative on Digital Content and Languages

Live video content is increasingly consumed over IP networks in addition to traditional broadcasting. The move to IP provides a huge opportunity to discover what people are watching in much greater breadth and depth than currently possible through interviews or set-top box based data gathering by rating organizations, because it allows direct analysis of consumer behavior via the logs they produce. The ViSTA-TV project proposes to gather consumers' anonymized viewing behavior and the actual video streams from broadcasters/IPTV-transmitters to combine them with enhanced electronic program guide information as the input for a holistic live-stream data mining analysis: the basis for an SME-driven market-place for TV viewing-behavior information. First, ViSTA-TV will employ the gathered information via a stream-analytics process to generate a high-quality linked open dataset (LOD) describing live TV programming. Second, combining the LOD with the behavioral information gathered, ViSTA-TV will be in the position to provide highly accurate market research information about viewing behavior that can be used for a variety of analyses of high interest to all participants in the TV-industry. This generates a novel, SME-driven market place for TV viewing-behavior data and analyses. Third, to gather anonymized behavioral information about viewers not using our IPTV-streams ViSTA-TV will employ the gathered information to build a recommendation service that exploits both usage information and personalized feature extraction in conjunction with existing meta-information to provide real-time viewing recommendations. Commercially, the revenues gathered in the market research activity will cross-subsidize the production of the open-sourced LOD stream. These results are made possible by scientific progress in data-stream mining consisting of advances in (1) data mining for tagging, recommendations, and behavioral analyses and (2) temporal/probabilistic RDF-triple stream processing.

Call for proposal

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University of Zurich
Ramistrasse 71
8006 Zurich

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Activity type
Higher or Secondary Education Establishments
Administrative Contact
Abraham Bernstein (Prof.)
EU contribution
No data

Participants (7)