View Selection in Semantic Web Databases

TL;DR

This paper presents new algorithms for selecting and recommending views in Semantic Web databases, effectively managing explicit and implicit RDF data to optimize query processing, storage, and maintenance costs.

Contribution

It introduces scalable view selection algorithms adapted for RDF data and a novel query reformulation method to handle implicit triples efficiently.

Findings

Algorithms scale beyond existing relational methods

Effective handling of implicit triples in view selection

Demonstrated improvements through experimental evaluation

Abstract

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.