# MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

arXiv: https://arxiv.org/abs/2201.04596 · HTML: https://mlsanity.com/paper/2201.04596/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [The Vadalog System: Datalog-based Reasoning for Knowledge Graphs](https://mlsanity.com/paper/1807.08709/) (2018)
- [Querying Log Data with Metric Temporal Logic (Technical Report)](https://mlsanity.com/paper/1703.08982/) (2017)
- [Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics (Full Version)](https://mlsanity.com/paper/1905.12990/) (2019)

## Built on by (most cited first, 7 of 7)

- [Goal-Driven Reasoning in DatalogMTL with Magic Sets](https://mlsanity.com/paper/2412.07259/) (2024)
- [On Deciding the Data Complexity of Answering Linear Monadic Datalog Queries with LTL Operators(Extended Version)](https://mlsanity.com/paper/2501.13762/) (2025)
- [Seminaive Materialisation in DatalogMTL](https://mlsanity.com/paper/2208.07100/) (2022)
- [Inconsistency Handling in DatalogMTL](https://mlsanity.com/paper/2505.10394/) (2025)
- [Compiling Metric Temporal Answer Set Programming](https://mlsanity.com/paper/2506.08150/) (2025)
- [Analysing Temporal Reasoning in Description Logics Using Formal Grammars](https://mlsanity.com/paper/2508.00575/) (2025)
- [Incremental Maintenance of DatalogMTL Materialisations](https://mlsanity.com/paper/2511.12169/) (2025)

## Abstract (arXiv)

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.