Left Bit Right: For SPARQL Join Queries with OPTIONAL Patterns (Left-outer-joins)

TL;DR

This paper introduces Left Bit Right (LBR), a novel optimization technique for SPARQL OPTIONAL pattern queries that significantly improves query processing speed by using graph-based representations and advanced pruning strategies.

Contribution

LBR is the first method to optimize well-designed nested BGP and OPTIONAL queries using supernode graphs and combined optimization strategies, outperforming existing RDF stores.

Findings

LBR processes complex queries up to 11 times faster than Virtuoso and MonetDB.

LBR performs on par with state-of-the-art systems for highly selective queries.

Evaluation on billion-triple RDF graphs demonstrates scalability and efficiency.

Abstract

SPARQL basic graph pattern (BGP) (a.k.a. SQL inner-join) query optimization is a well researched area. However, optimization of OPTIONAL pattern queries (a.k.a. SQL left-outer-joins) poses additional challenges, due to the restrictions on the \textit{reordering} of left-outer-joins. The occurrence of such queries tends to be as high as 50% of the total queries (e.g., DBPedia query logs). In this paper, we present \textit{Left Bit Right} (LBR), a technique for \textit{well-designed} nested BGP and OPTIONAL pattern queries. Through LBR, we propose a novel method to represent such queries using a graph of \textit{supernodes}, which is used to aggressively prune the RDF triples, with the help of compressed indexes. We also propose novel optimization strategies -- first of a kind, to the best of our knowledge -- that combine together the characteristics of \textit{acyclicity} of queries, \textit{minimality}, and \textit{nullification}, \textit{best-match} operators. In this paper, we focus on OPTIONAL patterns without UNIONs or FILTERs, but we also show how UNIONs and FILTERs can be handled with our technique using a \textit{query rewrite}. Our evaluation on RDF graphs of up to and over one billion triples, on a commodity laptop with 8 GB memory, shows that LBR can process \textit{well-designed} low-selectivity complex queries up to 11 times faster compared to the state-of-the-art RDF column-stores as Virtuoso and MonetDB, and for highly selective queries, LBR is at par with them.