Expressive Stream Reasoning with Laser

TL;DR

Laser is a new stream reasoner supporting an expressive fragment of LARS, enabling efficient, logic-based reasoning over large data streams for IoT and web applications.

Contribution

It introduces Laser, a novel reasoner with a unique evaluation procedure that significantly improves runtime performance for expressive stream reasoning.

Findings

Laser outperforms state-of-the-art systems like C-SPARQL and CQELS.

Laser enables reasoning on large data streams with improved efficiency.

The system supports a pragmatic fragment of LARS extending ASP.

Abstract

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.