Efficient OLAP Operations For RDF Analytics

International audienceRDF is the leading data model for the Semantic Web, and dedicated query languages such as SPARQL 1.1, featuring in particular aggregation, allow extracting information from RDF graphs. A framework for analytical processing of RDF data was introduced in [1], where analytical schemas and analytical queries (cubes) are fully redesigned for heterogeneous, semantic-rich RDF graphs. In this novel analytical setting, we consider the following optimization problem: how to reuse the materialized result of a given RDF analytical query (cube) in order to compute the answer to another cube. We provide view-based rewriting algorithms for these cube transformations, and demonstrate experimentally their practical interest

Opérations OLAP efficace pour l'analyse de données RDF

RDF is the leading data model for the Semantic Web, and dedicated query languages such as SPARQL 1.1, featuring in particular aggregation, allow extracting information from RDF graphs. A framework for analytical processing of RDF data was introduced in [1], where analytical schemas and analytical queries (cubes) are fully re-designed for heterogeneous, semantic-rich RDF graphs. In this novel analytical setting, we consider the following optimization problem: how to reuse the materialized result of a given analytical query (cube) in order to compute the answer to another analytical query obtained through a typical OLAP operation. We provide view-based rewriting algorithms for these query transformations, and demonstrate experimentally their practical interest