PRESTO: Probabilistic Cardinality Estimation for RDF Queries Based on Subgraph Overlapping

TL;DR

PRESTO introduces a probabilistic subgraph-based approach for RDF query cardinality estimation, improving accuracy over traditional triple-count methods especially for complex queries under memory constraints.

Contribution

It presents a novel probabilistic method that estimates RDF query cardinalities using subgraph counts, addressing limitations of existing approaches.

Findings

PRESTO outperforms existing methods in accuracy on YAGO datasets.

It effectively estimates complex RDF queries within memory bounds.

PRESTO reduces error accumulation in cardinality estimation.

Abstract

In query optimisation accurate cardinality estimation is essential for finding optimal query plans. It is especially challenging for RDF due to the lack of explicit schema and the excessive occurrence of joins in RDF queries. Existing approaches typically collect statistics based on the counts of triples and estimate the cardinality of a query as the product of its join components, where errors can accumulate even when the estimation of each component is accurate. As opposed to existing methods, we propose PRESTO, a cardinality estimation method that is based on the counts of subgraphs instead of triples and uses a probabilistic method to estimate cardinalities of RDF queries as a whole. PRESTO avoids some major issues of existing approaches and is able to accurately estimate arbitrary queries under a bound memory constraint. We evaluate PRESTO with YAGO and show that PRESTO is more accurate for both simple and complex queries.