Optimizing Aggregate SPARQL Queries Using Materialized RDF Views
During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.