Efficient Temporal Reasoning on Streams of Events with DOTR

Many ICT applications need to make sense of large volumes of streaming data to detect situations of interest and enable timely reactions. Stream Reasoning (SR) aims to combine the performance of stream/event processing and the reasoning expressiveness of knowledge representation systems by adopting Semantic Web standards to encode streaming elements. We argue that the mainstream SR model is not flexible enough to properly express the temporal relations common in many applications. We show that the model can miss relevant information and lead to inconsistent derivations. Moving from these premises, we introduce a novel SR model that provides expressive ontological and temporal reasoning by neatly decoupling their scope to avoid losses and inconsistencies. We implement the model in the DOTR system that defines ontological reasoning using Datalog rules and temporal reasoning using a Complex Event Processing language that builds on metric temporal logic. We demonstrate the expressiveness of our model through examples and benchmarks, and we show that DOTR outperforms state-of-the-art SR tools, processing data with millisecond latency.