XML parsing: a threat to database performance
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
MoteCare: an adaptive smart BAN health monitoring system
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Provenance-aware storage systems
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
A protocol for recording provenance in service-oriented grids
OPODIS'04 Proceedings of the 8th international conference on Principles of Distributed Systems
Towards low overhead provenance tracking in near real-time stream filtering
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
Performance evaluation of the karma provenance framework for scientific workflows
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
Advances and Challenges for Scalable Provenance in Stream Processing Systems
Provenance and Annotation of Data and Processes
The Foundations for Provenance on the Web
Foundations and Trends in Web Science
Visual debugging for stream processing applications
RV'10 Proceedings of the First international conference on Runtime verification
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Provenance becomes a critical requirement for healthcare IT infrastructures, especially when pervasive biomedical sensors act as a source of raw medical streams for large-scale, automated clinical decision support systems. Medical and legal requirements will make it obligatory for such systems to answer queries regarding the underlying data samples from which output alerts are derived, the IDs of the processing components used and the privileges of the individuals and software components accessing the medical data. Unfortunately, existing models of either annotation or process based provenance are designed for transaction-oriented systems and do not satisfy the unique requirements for systems processing high-volume, continuous medical streams. This paper proposes a simple, but useful, hybrid provenance model called Time-Value Centric (TVC) provenance.