Composite Events for Active Databases: Semantics, Contexts and Detection
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
On the complexity of privacy-preserving complex event processing
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Active complex event processing over event streams
Proceedings of the VLDB Endowment
Realtime healthcare services via nested complex event processing technology
Proceedings of the 15th International Conference on Extending Database Technology
Anomaly management using complex event processing: extending data base technology paper
Proceedings of the 16th International Conference on Extending Database Technology
Probabilistic inference of object identifications for event stream analytics
Proceedings of the 16th International Conference on Extending Database Technology
Utility-maximizing event stream suppression
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Model-based validation of streaming data: (industry article)
Proceedings of the 7th ACM international conference on Distributed event-based systems
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Our analysis of many real-world event based applications has revealed that existing Complex Event Processing technology (CEP), while effective for efficient pattern matching on event stream, is limited in its capability of reacting in realtime to opportunities and risks detected or environmental changes. We are the first to tackle this problem by providing active rule support embedded directly within the CEP engine, henceforth called Active Complex Event Processing technology, or short, Active CEP. We design the Active CEP model and associated rule language that allows rules to be triggered by CEP system state changes and correctly executed during the continuous query process. Moreover we design an Active CEP infrastructure, that integrates the active rule component into the CEP kernel, allowing fine-grained and optimized rule processing. We demonstrate the power of Active CEP by applying it to the development of a collaborative project with UMass Medical School, which detects potential threads of infection and reminds healthcare workers to perform hygiene precautions in real-time.