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ViSTA-TV - Video Stream Analytics for Viewers in the TV Industry (ViSTA-TV)
Date du début: 1 juin 2012, Date de fin: 31 mai 2014 PROJET  TERMINÉ 

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.



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