TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Expressing and optimizing sequence queries in database systems
ACM Transactions on Database Systems (TODS)
Order checking in a CPOE using event analyzer
Proceedings of the 14th ACM international conference on Information and knowledge management
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient pattern matching over event streams
Proceedings of the 2008 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
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
DejaVu: declarative pattern matching over live and archived streams of events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Sequenced event set pattern matching
Proceedings of the 14th International Conference on Extending Database Technology
Towards expressive publish/subscribe systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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In event pattern matching a sequence of input events is matched against a complex query pattern that specifies constraints on extent, order, values, and quantification of matching events. In this paper we propose a general pattern matching strategy that consists of a pre-processing step and a pattern matching step. Instead of eagerly matching incoming events, the pre-processing step buffers events in a match window to apply different pruning techniques (filtering, partitioning, and testing for necessary match conditions). In the second step, an event pattern matching algorithm, A, is called only for match windows that satisfy the necessary match conditions. This two-phase strategy with a lazy call of the matching algorithm significantly reduces the number of events that need to be processed by A as well as the number of calls to A. This is important since pattern matching algorithms tend to be expensive in terms of runtime and memory complexity, whereas the pre-processing can be done very efficiently. We conduct extensive experiments using real-world data with pattern matching algorithms for, respectively, automata and join trees. The experimental results confirm the effectiveness of our strategy for both types of pattern matching algorithms.