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In complex event processing (CEP), load shedding is performed to maintain agiven latency bound during overload situations when there is a limitation onresources. However, shedding load implies degradation in the quality of results(QoR). Therefore, it is crucial to perform load shedding in a way that has thelowest impact on QoR. Researchers, in the CEP domain, propose to drop eitherevents or partial matches (PMs) in overload cases. They assign utilities toevents or PMs by considering either the importance of events or the importanceof PMs but not both together. In this paper, we propose a load sheddingapproach for CEP systems that combines these approaches by assigning a utilityto an event by considering both the event importance and the importance of PMs.We adopt a probabilistic model that uses the type and position of an event in awindow and the state of a PM to assign a utility to an event corresponding toeach PM. We, also, propose an approach to predict a utility threshold that isused to drop the required amount of events to maintain a given latency bound.By extensive evaluations on two real-world datasets and several representativequeries, we show that, in the majority of cases, our load shedding approachoutperforms state-of-the-art load shedding approaches, w.r.t. QoR.

hSPICE: State-Aware Event Shedding in Complex Event Processing