Efficient OLAP Operations for RDF Analytics

Elham Akbari Azirani 1 François Goasdoué 1 Ioana Manolescu 1 Alexandra Roatis 1
1 OAK - Database optimizations and architectures for complex large data
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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 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.
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Elham Akbari Azirani, François Goasdoué, Ioana Manolescu, Alexandra Roatis. Efficient OLAP Operations for RDF Analytics. [Research Report] RR-8668, OAK team, Inria Saclay; INRIA. 2015. ⟨hal-01101843⟩

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