Efficient Temporal Datalog Materialisation for Composite Event Recognition

TL;DR

This paper presents a unified approach for composite event recognition by mapping various event specification languages into Temporal Datalog and introducing Streaming Trigger Graphs for efficient stream reasoning.

Contribution

It introduces a mapping of practical event specification languages into Temporal Datalog and proposes Streaming Trigger Graphs to enhance stream reasoning efficiency.

Findings

Mapping of event specification languages into Temporal Datalog improves comparability.

Streaming Trigger Graphs enable efficient materialisation for stream reasoning.

The approach generalizes across multiple practical event specification languages.

Abstract

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.