Exchange and Consumption of Huge RDF Data [chapter]

Miguel A. Martínez-Prieto, Mario Arias Gallego, Javier D. Fernández
<span title="">2012</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Huge RDF datasets are currently exchanged on textual RDF formats, hence consumers need to post-process them using RDF stores for local consumption, such as indexing and SPARQL query. This results in a painful task requiring a great effort in terms of time and computational resources. A first approach to lightweight data exchange is a compact (binary) RDF serialization format called HDT. In this paper, we show how to enhance the exchanged HDT with additional structures to support some basic
more &raquo; ... of SPARQL query resolution without the need of "unpacking" the data. Experiments show that i) with an exchanging efficiency that outperforms universal compression, ii) post-processing now becomes a fast process which iii) provides competitive query performance at consumption. 1
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-30284-8_36">doi:10.1007/978-3-642-30284-8_36</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tyhk622z4re2hmvf33ilutewca">fatcat:tyhk622z4re2hmvf33ilutewca</a> </span>
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