Constraint-aware event stream processing
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
Recognizing patterns in streams with imprecise timestamps
Proceedings of the VLDB Endowment
Sequenced event set pattern matching
Proceedings of the 14th International Conference on Extending Database Technology
Complex event pattern detection over streams with interval-based temporal semantics
Proceedings of the 5th ACM international conference on Distributed event-based system
Constraint-aware complex event pattern detection over streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Run-time composite event recognition
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Efficient event pattern matching with match windows
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
OECEP: enriching complex event processing with domain knowledge from ontologies
Proceedings of the Fifth Balkan Conference in Informatics
A multi-dimensional and event-based model for trust computation in the social web
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
A query-matching mechanism over out-of-order event stream in IOT
International Journal of Ad Hoc and Ubiquitous Computing
Recognizing patterns in streams with imprecise timestamps
Information Systems
Efficient recovery of missing events
Proceedings of the VLDB Endowment
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Detecting complex patterns in event streams, i.e., complex event processing (CEP), has become increasingly important for modern enterprises to react quickly to critical situations. In many practical cases business events are generated based on pre-defined business logics. Hence constraints, such as occurrence and order constraints, often hold among events. Reasoning using these known constraints enables us to predict the non-occurrences of certain future events, thereby helping us to identify and then terminate the long running query processes that are guaranteed to not lead to successful matches. In this work, we focus on exploiting event constraints to optimize CEP over large volumes of business transaction streams. Since the optimization opportunities arise at runtime, we develop a runtime query unsatisfiability (RunSAT) checking technique that detects optimal points for terminating query evaluation. To assure efficiency of RunSAT checking, we propose mechanisms to precompute the query failure conditions to be checked at runtime. This guarantees a constant-time RunSAT reasoning cost, making our technique highly scalable. We realize our optimal query termination strategies by augmenting the query with Event-Condition-Action rules encoding the pre-computed failure conditions. This results in an event processing solution compatible with state-of-the-art CEP architectures. Extensive experimental results demonstrate that significant performance gains are achieved, while the optimization overhead is small.