We are working on a new kind of prototype dedicated to broadcasters: the system is able to analyze different kind of flows (video, speech to text, structured feeds, social network feeds, etc) in order to identify “patterns” like specific images or semantic correlations. The system is based on the WASP infrastructure and benefits of all its peculiar features: data consistency, massive ingestion, machine learning algorithms with real time and post processing capabilities.
Considering the broadcasters use cases,  where video or audio codecs are the main flows, the system is able to:

• analyze the specific video flow frame per frame identifying a reference image pattern (a logo, a face, or in general a pre-defined image) with machine learning methods and/or with embeddable third parties  “plug-ins”
• produce massive meta-tags identified within the specific pattern-frame matching
• classify and elaborate the produced tags (associated to specific video contents) and eventually completed by Open Data Semantic correaltions, creating a semantic-correlated knowledge base, function that is possible thanks to the Sophia Semantic Engine, a library developed by CELI that is fully integrated into the system.

Take a look to the overall logical design:
 

 
The system is designed with a scale-out pattern architecture and it could be used in full “enterprise” environments without losing performances even if with high input throughputs.
This kind of system offers many high value features that could be used to enhance the legacy systems normally used in the broadcasters application universe. The use cases could be different and interesting:

• we could deliver a totally new tool for the video-editing process: the editor will be able to search into a large volume video library using new keywords associated to a specific content in any kind of video or audio fragment
• a broadcaster with a great historical video repository is now able to monetize his copyright videos in several ways (“Stock Video” e-commerce, etc)
• new kind of applications will be available like real time advertising or product displacement strategies based on the user profile and on the specific content that is viewed at the moment

This is a very challenging domain that will totally enhance our platform capabilities not only in terms of performance but also from the “algo” point of view.