# Stream Reasoning in Temporal Datalog

arXiv: https://arxiv.org/abs/1711.04013 · HTML: https://mlsanity.com/paper/1711.04013/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Temporal Inductive Logic Reasoning over Hypergraphs](https://mlsanity.com/paper/2206.05051/) (2022)
- [The Transformation Logics](https://mlsanity.com/paper/2304.09639/) (2023)
- [FAERDXEL: An Expert System for Danish Traffic Law](https://mlsanity.com/paper/2410.03560/) (2024)
- [Efficient Temporal Datalog Materialisation for Composite Event Recognition](https://mlsanity.com/paper/2605.02488/) (2026)

## Abstract (arXiv)

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).