{"paper":{"title":"Compressed Vertical Partitioning for Full-In-Memory RDF Management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.IR"],"primary_cat":"cs.DB","authors_text":"Gonzalo Navarro, Javier D. Fern\\'andez, Miguel A. Mart\\'inez-Prieto, Nieves R. Brisaboa, Sandra \\'Alvarez-Garc\\'ia","submitted_at":"2013-10-18T08:58:01Z","abstract_excerpt":"The Web of Data has been gaining momentum and this leads to increasingly publish more semi-structured datasets following the RDF model, based on atomic triple units of subject, predicate, and object. Although it is a simple model, compression methods become necessary because datasets are increasingly larger and various scalability issues arise around their organization and storage. This requirement is more restrictive in RDF stores because efficient SPARQL resolution on the compressed RDF datasets is also required.\n  This article introduces a novel RDF indexing technique (called k2-triples) su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.4954","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}