  In this paper, we present a novel approach -- called WaterFowl -- for the
storage of RDF triples that addresses some key issues in the contexts of big
data and the Semantic Web. The architecture of our prototype, largely based on
the use of succinct data structures, enables the representation of triples in a
self-indexed, compact manner without requiring decompression at query answering
time. Moreover, it is adapted to efficiently support RDF and RDFS entailment
regimes thanks to an optimized encoding of ontology concepts and properties
that does not require a complete inference materialization or extensive query
rewriting algorithms. This approach implies to make a distinction between the
terminological and the assertional components of the knowledge base early in
the process of data preparation, i.e., preprocessing the data before storing it
in our structures. The paper describes the complete architecture of this system
and presents some preliminary results obtained from evaluations conducted on
our first prototype.
