Supporting views in data stream management systems
ACM Transactions on Database Systems (TODS)
Reliable complex event detection for pervasive computing
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
An extensible test framework for the Microsoft StreamInsight query processor
Proceedings of the Third International Workshop on Testing Database Systems
High-performance dynamic pattern matching over disordered streams
Proceedings of the VLDB Endowment
Proceedings of the 5th ACM international conference on Distributed event-based system
An approach for more efficient energy consumption based on real-time situational awareness
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Retractable complex event processing and stream reasoning
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Transactional stream processing
Proceedings of the 15th International Conference on Extending Database Technology
Mining frequent itemsets over tuple-evolving data streams
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Ariadne: managing fine-grained provenance on data streams
Proceedings of the 7th ACM international conference on Distributed event-based systems
Proceedings of the 7th ACM international conference on Distributed event-based systems
Scalable progressive analytics on big data in the cloud
Proceedings of the VLDB Endowment
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Data stream processing systems have become ubiquitous in academic [1, 2, 5, 6] and commercial [11] sectors, with application areas that include financial services, network traffic analysis, battlefield monitoring and traffic control [3]. The append-only model of streams implies that input data is immutable and therefore always correct. But in practice, streaming data sources often contend with noise (e.g., embedded sensors) or data entry errors (e.g., financial data feeds) resulting in erroneous inputs and therefore, erroneous query results. Many data stream sources (e.g., commercial ticker feeds) issue "revision tuples" (revisions) that amend previously issued tuples (e.g. erroneous share prices). Ideally, any stream processing engine should process revision inputs by generating revision outputs that correct previous query results. We know of no stream processing system that presently has this capability.