High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient pattern matching over event streams
Proceedings of the 2008 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
DejaVu: declarative pattern matching over live and archived streams of events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Distributed event stream processing with non-deterministic finite automata
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
TESLA: a formally defined event specification language
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Efficiently correlating complex events over live and archived data streams
Proceedings of the 5th ACM international conference on Distributed event-based system
Partition and compose: parallel complex event processing
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Auto-parallelizing stateful distributed streaming applications
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Hi-index | 0.00 |
Recognition of patterns in event streams has become important in many application areas of Complex Event Processing (CEP) including financial markets, electronic health-care systems, and security monitoring systems. In most applications, patterns have to be detected continuously and in real-time over streams that are generated at very high rates, imposing high-performance requirements on the underlying CEP system. For scaling CEP systems to increasing workloads, parallel pattern matching techniques that can exploit multi-core processing opportunities are needed. In this paper, we propose RIP - a Run-based Intra-query Parallelism technique for scalable pattern matching over event streams. RIP distributes input events that belong to individual run instances of a pattern's Finite State Machine (FSM) to different processing units, thereby providing fine-grained partitioned data parallelism. We compare RIP to a state-based alternative which partitions individual FSM states to different processing units instead. Our experiments demonstrate that RIP's partitioned parallelism approach outperforms the pipelined parallelism approach of this state-based alternative, achieving near-linear scalability that is independent from the query pattern definition.