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Operational BI platform for video analytics
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Source International Conference on Management of Emergent Digital EcoSystems archive
Proceedings of the International Conference on Management of Emergent Digital EcoSystems table of contents
France
SESSION: Multimedia and virtual worlds (MVW) table of contents
Article No. 27  
Year of Publication: 2009
ISBN:978-1-60558-829-2
Authors
Qiming Chen  HP Labs, Palo Alto, CA
Meichun Hsu  HP Labs, Palo Alto, CA
Rui Liu  HP Labs, Beijing, China
Tao Yu  HP Labs, Beijing, China
Qinghu Li  HP Labs, Beijing, China
Weihong Wang  HP Labs, Beijing, China
Sponsor
: The French Chapter of ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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