Efficient Temporal Datalog Materialisation for Composite Event Recognition
For developers of stream reasoning systems, this work provides a unified framework that simplifies comparison and implementation of composite event recognition across different event specification languages.
The paper maps fragments of prominent event specification languages into Temporal Datalog with stratified negation, and proposes Streaming Trigger Graphs for efficient materialisation, enabling a uniform composite event recognition mechanism that generalises across multiple languages.
Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typically studied in isolation, complicating their comparison in terms of expressivity and obscuring the scope of their associated stream reasoners. To mitigate this issue, we map practical fragments of prominent event specification languages into Temporal Datalog->-, a temporal Datalog with stratified negation and no future dependencies. To support efficient stream reasoning over Temporal Datalog->-, we propose Streaming Trigger Graphs, an extension of a state-of-the-art technique for Datalog materialisation. Our approach yields a uniform composite event recognition mechanism that has the potential to generalise across a wide range of practical event specification languages.