# Efficient Temporal Datalog Materialisation for Composite Event Recognition

arXiv: https://arxiv.org/abs/2605.02488 · HTML: https://mlsanity.com/paper/2605.02488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 15 of 15)

- [The Vadalog System: Datalog-based Reasoning for Knowledge Graphs](https://mlsanity.com/paper/1807.08709/) (2018)
- [Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)](https://mlsanity.com/paper/1511.08915/) (2015)
- [Expressive Stream Reasoning with Laser](https://mlsanity.com/paper/1707.08876/) (2017)
- [Materializing Knowledge Bases via Trigger Graphs](https://mlsanity.com/paper/2102.02753/) (2021)
- [Stream Reasoning in Temporal Datalog](https://mlsanity.com/paper/1711.04013/) (2017)
- [A Distributed Approach to LARS Stream Reasoning (System paper)](https://mlsanity.com/paper/1907.12344/) (2019)
- [Combining Event Calculus and Description Logic Reasoning via Logic Programming](https://mlsanity.com/paper/2109.04803/) (2021)
- [Ticker: A System for Incremental ASP-based Stream Reasoning](https://mlsanity.com/paper/1707.05304/) (2017)
- [Online Event Recognition from Moving Vehicles: Application Paper](https://mlsanity.com/paper/1907.11007/) (2019)
- [CORE: a Complex Event Recognition Engine](https://mlsanity.com/paper/2111.04635/) (2021)
- [The Window Validity Problem in Rule-Based Stream Reasoning](https://mlsanity.com/paper/1808.02291/) (2018)
- [Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue](https://mlsanity.com/paper/2304.05459/) (2023)
- [Practical Reasoning in DatalogMTL](https://mlsanity.com/paper/2401.02869/) (2024)
- [Complex Event Recognition with Symbolic Register Transducers: Extended Technical Report](https://mlsanity.com/paper/2407.02884/) (2024)
- [The Temporal Vadalog System: Temporal Datalog-based Reasoning](https://mlsanity.com/paper/2412.13019/) (2024)

## Abstract (arXiv)

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.