Composite event detection (CED) is the task of identifying combinations of events which are meaningful with respect to program-defined patterns. Recent research in event-based programming has focused on language design (in different paradigms), leading to a wealth of prototype programming models and languages. However, implementing CED in an efficient and scalable manner remains an under-addressed problem. In fact, the lack of scalable algorithms is the main roadblock to incorporating support for more expressive event patterns into prominent event-based programming languages. This lack of scalable algorithms is a particularly acute problem in event stream processing, where event patterns can additionally be specified over time windows. In this paper we describe GenTrie, a deterministic trie-based algorithm for CED. We describe how complex event patterns are split, how each sub-pattern maps to a node in the trie, and demonstrate through empirical evaluation that GenTrie has higher throughput than current implementations of related languages. © 2010 Springer-Verlag.
CITATION STYLE
Jayaram, K. R., & Eugster, P. (2010). Scalable efficient composite event detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6116 LNCS, pp. 168–182). https://doi.org/10.1007/978-3-642-13414-2_12
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