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Complex Event Processing (CEP) is an emerging field with importantapplications in many areas. CEP systems collect events arriving from input datastreams and use them to infer more complex events according to predefinedpatterns. The Non-deterministic Finite Automaton (NFA) is one of the mostpopular mechanisms on which such systems are based. During the event detectionprocess, NFAs incrementally extend previously observed partial matches until afull match for the query is found. As a result, each arriving event needs to beprocessed to determine whether a new partial match is to be initiated or anexisting one extended. This method may be highly inefficient when many of theevents do not result in output matches. We present a lazy evaluation mechanismthat defers processing of frequent event types and stores them internally uponarrival. Events are then matched in ascending order of frequency, thusminimizing potentially redundant computations. We introduce a Lazy Chain NFA,which utilizes the above principle, and does not depend on the underlyingpattern structure. An algorithm for constructing a Lazy Chain NFA for commonpattern types is presented, including conjunction, negation and iteration.Finally, we experimentally evaluate our mechanism on real-world stock tradingdata. The results demonstrate a performance gain of two orders of magnitudeover traditional NFA-based approaches, with significantly reduced memoryresource requirements.

Efficient Detection of Complex Event Patterns Using Lazy Chain Automata