# Datalog Materialisation in Distributed RDF Stores with Dynamic Data Exchange

## TL;DR

This paper introduces a scalable distributed reasoning algorithm for RDF stores that can handle arbitrary datalog rules, extending existing dynamic data exchange methods, and demonstrates its effectiveness on large datasets.

## Contribution

It extends the dynamic data exchange approach to support arbitrary datalog rules in distributed RDF reasoning, maintaining inference properties and scalability.

## Key findings

- Algorithm scales well to large RDF datasets
- Supports arbitrary datalog rules in distributed reasoning
- Preserves properties like nonrepetition of inferences

## Abstract

Several centralised RDF systems support datalog reasoning by precomputing and storing all logically implied triples using the wellknown seminaive algorithm. Large RDF datasets often exceed the capacity of centralised RDF systems, and a common solution is to distribute the datasets in a cluster of shared-nothing servers. While numerous distributed query answering techniques are known, distributed seminaive evaluation of arbitrary datalog rules is less understood. In fact, most distributed RDF stores either support no reasoning or can handle only limited datalog fragments. In this paper we extend the dynamic data exchange approach for distributed query answering by Potter et al. [12] to a reasoning algorithm that can handle arbitrary rules while preserving important properties such as nonrepetition of inferences. We also show empirically that our algorithm scales well to very large RDF datasets