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
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 SHAMAN - Symbolic and Human-centric view of dAta MANagement
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : 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 .
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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⟩

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