Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Composite Event Specification in Active Databases: Model & Implementation
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Composite Events for Active Databases: Semantics, Contexts and Detection
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Design and Implementation of a Sequence Database System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
On the Semantics of Complex Events in Active Database Management Systems
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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
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
DejaVu: declarative pattern matching over live and archived streams of events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Towards expressive publish/subscribe systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Efficient event pattern matching with match windows
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Supporting phase management in stream applications
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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Event pattern matching is a query technique where a sequence of input events is matched against a complex pattern that specifies constraints on extent, order, values, and quantification of matching events. The increasing importance of such query techniques is underpinned by a significant amount of research work, the availability of commercial products, and by a recent proposal to extend SQL for event pattern matching. The proposed SQL extension includes an operator PERMUTE, which allows to express patterns that match any permutation of a set of events. No implementation of this operator is known to the authors. In this paper, we study the sequenced event set pattern matching problem, which is the problem of matching a sequence of input events against a complex pattern that specifies a sequence of sets of events rather than a sequence of single events. Similar to the PERMUTE operator, events that match with a set specified in the pattern can occur in any permutation, whereas events that match with different sets have to be strictly consecutive, following the order of the sets in the pattern specification. We formally define the problem of sequenced event set pattern matching, propose an automaton-based evaluation algorithm, and provide a detailed analysis of its runtime complexity. An empirical evaluation with real-world data shows that our algorithm outperforms a brute force approach that uses existing techniques to solve the sequenced event set pattern matching problem, and it validates the results from our complexity analysis.