Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks

TL;DR

This paper explores training deep neural networks, specifically CNNs and RNNs, to approximate the reasoning capabilities of LASER, a stream reasoner, aiming to enable faster real-time data processing in complex scenarios.

Contribution

It introduces a novel approach of using neural networks to emulate LASER's reasoning, enhancing processing speed for stream reasoning tasks.

Findings

Neural networks can approximate LASER's reasoning with high accuracy.

Deep learning models significantly improve reasoning speed over traditional systems.

The approach enables real-time processing in high-throughput data streams.

Abstract

With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to process and reason with such data in real time. Whereas reasoning over time-annotated data with background knowledge may be challenging, due to the volume and velocity in which such data is being produced, such complex reasoning is necessary in scenarios where agents need to discover potential problems and this cannot be done with simple stream processing techniques. Stream Reasoners aim at bridging this gap between reasoning and stream processing and LASER is such a stream reasoner designed to analyse and perform complex reasoning over streams of data. It is based on LARS, a rule-based logical language extending Answer Set Programming, and it has shown better runtime results than other state-of-the-art stream reasoning systems. Nevertheless, for high levels of data throughput even LASER may be unable to compute answers in a timely fashion. In this paper, we study whether Convolutional and Recurrent Neural Networks, which have shown to be particularly well-suited for time series forecasting and classification, can be trained to approximate reasoning with LASER, so that agents can benefit from their high processing speed.