# Accelerating Partial Evaluation in Distributed SPARQL Query Evaluation

## TL;DR

This paper enhances distributed SPARQL query processing by optimizing partial evaluation through structural filtering, efficient assembly, and communication strategies, leading to improved performance and scalability.

## Contribution

It introduces a novel framework that exploits partial match structures, optimizes communication, and evaluates partitioning strategies for faster distributed SPARQL query evaluation.

## Key findings

- Significant performance improvements over previous methods.
- Effective filtering reduces irrelevant partial matches.
- Partitioning strategies impact overall efficiency.

## Abstract

Partial evaluation has recently been used for processing SPARQL queries over a large resource description framework (RDF) graph in a distributed environment. However, the previous approach is inefficient when dealing with complex queries. In this study, we further improve the "partial evaluation and assembly" framework for answering SPARQL queries over a distributed RDF graph, while providing performance guarantees. Our key idea is to explore the intrinsic structural characteristics of partial matches to filter out irrelevant partial results, while providing performance guarantees on a network trace (data shipment) or the computational cost (response time). We also propose an efficient assembly algorithm to utilize the characteristics of partial matches to merge them and form final results. To improve the efficiency of finding partial matches further, we propose an optimization that communicates variables' candidates among sites to avoid redundant computations. In addition, although our approach is partitioning-tolerant, different partitioning strategies result in different performances, and we evaluate different partitioning strategies for our approach. Experiments over both real and synthetic RDF datasets confirm the superiority of our approach.