High-Performance Dynamic Pattern Matching over Disordered Streams

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 patterndetection semantics for AFAs, with new algorithms that allow a very efficient impleme...

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Badrish Chandramouli, Jonathan Goldstein, David Maier
• Keywords: Current Pattern-detection Proposals, Dynamic Patterns, Input, PVLDB 2010