Resumen:
RDF compression and querying are consolidated topics in the Web
of Data, with a plethora of solutions to efficiently store and query
static datasets. However, as RDF data changes along time, it
becomes necessary to keep different versions of RDF datasets, in
what is called an RDF archive. For large RDF datasets, naive techniques to store these versions lead to significant scalability problems.
In this paper we present v-RDF-SI, one of the first RDF archiving
solutions that aims at joining both compression and fast querying. In
v-RDF-SI, we extend existing RDF representations based on compact
data structures to provide efficient support of version-based queries
in compressed space. We present two implementations of v-RDF-SI,named v-RDFCSA and v-HDT, based respectively on RDFCSA (an RDF
self-index) and HDT (a W3C-supported compressed RDF representation).
We experimentally evaluate v-RDF-SI over a public benchmark named
BEAR, showing that v-RDF-SI drastically reduces space requirements.
