Abstract
We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTEC_inc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTEC_inc deals with delays and revisions in a more efficient way, by updating only the affected queries. We compare RTEC_inc and RTEC experimentally using real-world and synthetic datasets. The results are compatible with our complexity analysis and show that RTEC_inc outperforms RTEC in many practical cases.
Keywords
- Knowledge Representation and Reasoning: General
- Knowledge Representation and Reasoning: KRR: Logic programming
- Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning
- Knowledge Representation and Reasoning: KRR: Reasong about actions
- Knowledge Representation and Reasoning: KRR: Reasoning about knowledge and belief
