Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Path sharing and predicate evaluation for high-performance XML filtering
ACM Transactions on Database Systems (TODS)
Exploiting k-constraints to reduce memory overhead in continuous queries over data streams
ACM Transactions on Database Systems (TODS)
Flexible time management in data stream systems
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Revision Processing in a Stream Processing Engine: A High-Level Design
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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
Scalable regular expression matching on data streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Sequence Pattern Query Processing over Out-of-Order Event Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Microsoft CEP server and online behavioral targeting
Proceedings of the VLDB Endowment
On-the-fly progress detection in iterative stream queries
Proceedings of the VLDB Endowment
Towards expressive publish/subscribe systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Semantics of data streams and operators
ICDT'05 Proceedings of the 10th international conference on Database Theory
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
An extensibility approach for spatio-temporal stream processing using microsoft streaminsight
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Large-scale continuous subgraph queries on streams
Proceedings of the first annual workshop on High performance computing meets databases
Capturing episodes: may the frame be with you
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Proceedings of the 7th ACM international conference on Distributed event-based systems
Scalable XML query processing using parallel pushdown transducers
Proceedings of the VLDB Endowment
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Current pattern-detection proposals for streaming data recognize the need to move beyond a simple regular-expression model over strictly ordered input. We continue in this direction, relaxing restrictions present in some models, removing the requirement for ordered input, and permitting stream revisions (modification of prior events). Further, recognizing that patterns of interest in modern applications may change frequently over the lifetime of a query, we support updating of a pattern specification without blocking input or restarting the operator. Our new pattern operator (called AFA) is a streaming adaptation of a non-deterministic finite automaton (NFA) where additional schema-based user-defined information, called a register, is accessible to NFA transitions during execution. AFAs support dynamic patterns, where the pattern itself can change over time. We propose clean order-agnostic pattern-detection semantics for AFAs, with new algorithms that allow a very efficient implementation, while retaining significant expressiveness and supporting native handling of out-of-order input, stream revisions, dynamic patterns, and several optimizations. Experiments on Microsoft StreamInsight show that we achieve event rates of more than 200K events/sec (up to 5x better than simpler schemes). Our dynamic patterns give up to orders-of-magnitude better throughput than solutions such as operator restart, and our other optimizations are very effective, incurring low memory and latency.