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Current "data deluge" has flooded the Web of Data with very large RDFdatasets. They are hosted and queried through SPARQL endpoints which act asnodes of a semantic net built on the principles of the Linked Data project.Although this is a realistic philosophy for global data publishing, its queryperformance is diminished when the RDF engines (behind the endpoints) managethese huge datasets. Their indexes cannot be fully loaded in main memory, hencethese systems need to perform slow disk accesses to solve SPARQL queries. Thispaper addresses this problem by a compact indexed RDF structure (calledk2-triples) applying compact k2-tree structures to the well-knownvertical-partitioning technique. It obtains an ultra-compressed representationof large RDF graphs and allows SPARQL queries to be full-in-memory performedwithout decompression. We show that k2-triples clearly outperformsstate-of-the-art compressibility and traditional vertical-partitioning queryresolution, remaining very competitive with multi-index solutions.

Compressed k2-Triples for Full-In-Memory RDF Engines