Nested relation based database knowledge representation
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
User-Defined Table Operators: Enhancing Extensibility for ORDBMS
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A Transactional Model for Long-Running Activities
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Inter-Enterprise Collaborative Business Process Management
Proceedings of the 17th International Conference on Data Engineering
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
ACM Computing Surveys (CSUR)
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Data-Continuous SQL Process Model
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
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Multi-camera based video object tracking is a multi-stream data fusion and analysis problem. With the current technology, video analysis software architecture generally separates the analytics layer from the data management layer, which has become the performance bottleneck because of large scaled data transfer, inefficient data access and duplicate data buffering and management. Motivated by providing a convergent platform, we use user-defined Relation Valued Functions (RVFs) to have visual data computation naturally integrated to SQL queries, and pushed down to the database engine; we model complex applications with general graph based data-flows and control-flows at the process level where "actions" are performed by RVFs and "linked" in SQL queries. We further introduce Stream Query Process with stream data input and continuous execution. Our solutions to multi-camera video surveillance also include a new tracking method that is based on P2P time-synchronization of video streams and P2P target fusion. These techniques represent a major shift in process management from one-time execution to data stream driven, open-ended execution, and constitute a novel step to the use of a query engine for running processes, towards the "In-DB Streaming" paradigm. We have prototyped the proposed approaches by extending the open-sourced database engine Postgres, and plan to transfer the implementation to a commercial and proprietary parallel database system. The empirical study in a surveillance setting reveals their advantages in scalability, real-time performance and simplicity.