High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Runtime Semantic Query Optimization for Event Stream Processing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On Supporting Kleene Closure over Event Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The VLDB Journal — The International Journal on Very Large Data Bases
A reference architecture for Event Processing
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
Enabling knowledge-based complex event processing
Proceedings of the 2010 EDBT/ICDT Workshops
TESLA: a formally defined event specification language
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Enriching events to support hospital care
Proceedings of the 7th Middleware Doctoral Symposium
A rule-based language for complex event processing and reasoning
RR'10 Proceedings of the Fourth international conference on Web reasoning and rule systems
Event Processing in Action
Recognizing patterns in streams with imprecise timestamps
Proceedings of the VLDB Endowment
EP-SPARQL: a unified language for event processing and stream reasoning
Proceedings of the 20th international conference on World wide web
TMS-RFID: Temporal management of large-scale RFID applications
Information Systems Frontiers
Efficient semantic event processing: lessons learned in user interface integration
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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With the increasing adoption of an event-based perspective in many organizations, the demands for automatic processing of events are becoming more sophisticated. Although complex event processing systems can process events in near real-time, these systems rely heavily upon human domain experts. This becomes an issue in application areas that are rich in specialized domain knowledge and background information, such as clinical environments. We utilize a framework of four techniques to enhance complex event processing with domain knowledge from ontologies to address this issue. We realize this framework in our novel approach of ontology-supported complex event processing, which stands in contrast to related approaches and emphasizes the strengths of current advances in the individual fields of complex event processing and ontologies. Experimental results from the implementation of our approach based on a state-of-the-art system show its feasibility and indicate the direction for future research.