Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1643823.1643857 | MEDES |
Keywords | Field | DocType |
reduced data,analytical data,database engine,video analytics,video storage,video analysis,operational bi platform,fast data access,video analysis computation,data loading,video frame,data aggregation,domain knowledge,database system,business intelligence,data transfer,near real time,real time processing,value function,data access,system integration,user defined function | SQL,Data mining,Domain knowledge,Computer science,Database engine,User-defined function,Business intelligence,Analytics,Data access,Scalability | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiming Chen | 1 | 2010 | 233.16 |
| Meichun Hsu | 2 | 3437 | 778.34 |
| Rui Liu | 3 | 25 | 6.45 |
| Tao Yu | 4 | 0 | 0.34 |
| Qinghu Li | 5 | 12 | 2.75 |
| Weihong Wang | 6 | 582 | 44.63 |