# Expressive Stream Reasoning with Laser

arXiv: https://arxiv.org/abs/1707.08876 · HTML: https://mlsanity.com/paper/1707.08876/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

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

- [I-DLV-sr: A Stream Reasoning System based on I-DLV](https://mlsanity.com/paper/2108.02797/) (2021)
- [Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks](https://mlsanity.com/paper/2106.08457/) (2021)
- [Chasing Streams with Existential Rules](https://mlsanity.com/paper/2205.02220/) (2022)
- [A Formal Comparison between Datalog-based Languages for Stream Reasoning (extended version)](https://mlsanity.com/paper/2208.12726/) (2022)
- [Incremental, inconsistency-resilient reasoning over Description Logic Abox streams](https://mlsanity.com/paper/2603.01799/) (2026)
- [Efficient Temporal Datalog Materialisation for Composite Event Recognition](https://mlsanity.com/paper/2605.02488/) (2026)
- [LLMs for Executable Multi-Agent System Specification Generation](https://mlsanity.com/paper/2609.34619/) (2026)

## Abstract (arXiv)

An increasing number of use cases require a timely extraction of non-trivial knowledge from semantically annotated data streams, especially on the Web and for the Internet of Things (IoT). Often, this extraction requires expressive reasoning, which is challenging to compute on large streams. We propose Laser, a new reasoner that supports a pragmatic, non-trivial fragment of the logic LARS which extends Answer Set Programming (ASP) for streams. At its core, Laser implements a novel evaluation procedure which annotates formulae to avoid the re-computation of duplicates at multiple time points. This procedure, combined with a judicious implementation of the LARS operators, is responsible for significantly better runtimes than the ones of other state-of-the-art systems like C-SPARQL and CQELS, or an implementation of LARS which runs on the ASP solver Clingo. This enables the application of expressive logic-based reasoning to large streams and opens the door to a wider range of stream reasoning use cases.