Operational BI platform for video analytics

  • Authors:

  • Qiming Chen;Meichun Hsu;Rui Liu;Tao Yu;Qinghu Li;Weihong Wang

  • Affiliations:

  • HP Labs, Palo Alto, CA;HP Labs, Palo Alto, CA;HP Labs, Beijing, China;HP Labs, Beijing, China;HP Labs, Beijing, China;HP Labs, Beijing, China

  • Venue:
  • Proceedings of the International Conference on Management of Emergent Digital EcoSystems
  • Year:
  • 2009

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Abstract

Video analytics is a data-intensive and knowledge-rich computation chain from collected video frames to high-level scene and behavior descriptions. The platform separation of video storage and video analysis, as it is now, has become the major bottleneck for scalability, efficiency and effectiveness of video analysis. We solve this problem by (a) completely pushing down video analysis computation to the database engine for fast data access and reduced data transfer; (b) systematically managing domain knowledge and context information, and consistently applying them to video analysis; (c) combining multilevel, multidimensional analytics with data loading for "just-in-time" meta-data materialization; (d) supporting analytical data streaming by database engine, towards a new paradigm for Operational Business Intelligence (OpBI). An OpBI system integrates the management of data, knowledge and analytics programs, along the canonical "eco-chain" of information abstraction, derivation, induction, and feedback. Then we focus on extending the query engine, the SQL framework and the UDF (User Defined Function) technology to support real-time, process-level and data streaming based OpBI, resulting in a highly efficient system contained entirely in a database system. Our experiment al results reveal that in-DB streaming and materializing meta-data, aggregates and other commonly interested analysis results along data loading, effectively enable near real-time analysis, and thus confirm the advantages of extending DB-engine to support OpBI.