Efficient Query Processing for SPARQL Federations with Replicated Fragments

TL;DR

FEDRA is a framework that enhances SPARQL federation query processing by leveraging client-side fragment replication to improve performance and data availability, reducing reliance on public endpoints.

Contribution

It introduces FEDRA, a novel source selection algorithm that optimizes query execution in federated SPARQL environments with replicated fragments.

Findings

Reduces number of public endpoints used during queries

Decreases query execution time

Lowers intermediate result sizes

Abstract

Low reliability and availability of public SPARQL endpoints prevent real-world applications from exploiting all the potential of these querying infras-tructures. Fragmenting data on servers can improve data availability but degrades performance. Replicating fragments can offer new tradeoff between performance and availability. We propose FEDRA, a framework for querying Linked Data that takes advantage of client-side data replication, and performs a source selection algorithm that aims to reduce the number of selected public SPARQL endpoints, execution time, and intermediate results. FEDRA has been implemented on the state-of-the-art query engines ANAPSID and FedX, and empirically evaluated on a variety of real-world datasets.