Efficient OLAP Operations For RDF Analytics

Elham Akbari-Azirani (1, 2) , François Goasdoué (3, 1) , Ioana Manolescu (2, 1) , Alexandra Roatis (1, 2)

1 OAK - Database optimizations and architectures for complex large data
2 LRI - Laboratoire de Recherche en Informatique
3 SHAMAN - Symbolic and Human-centric view of dAta MANagement

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 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 .

Domains: Databases [cs.DB]

Elham Akbari-Azirani, François Goasdoué, Ioana Manolescu, Alexandra Roatis. Efficient OLAP Operations For RDF Analytics. International Workshop on Data Engineering meets the Semantic Web (DESWeb), Apr 2015, Seoul, South Korea. ⟨10.1109/ICDEW.2015.7129548⟩. ⟨hal-01187448⟩