# A Distributed Approach to LARS Stream Reasoning (System paper)

## TL;DR

This paper introduces a distributed system for stream reasoning using the LARS language, improving performance and scalability over traditional stand-alone solvers by leveraging distributed computation and interval-based semantics.

## Contribution

It proposes a novel distributed approach to stream reasoning with interval-based semantics, enhancing efficiency and scalability for complex, high-rate data streams.

## Key findings

- Distributed stream reasoning outperforms stand-alone solvers
- Interval-based semantics reduces network traffic
- System scales with data complexity and rate

## Abstract

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing. Under consideration for acceptance in Theory and Practice of Logic Programming.