Exchange and Consumption of Huge RDF Data

  • Miguel A. Martínez-Prieto
  • Mario Arias Gallego
  • Javier D. Fernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)


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 forms 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.


Data Provider SPARQL Query Triple Pattern Adjacency List Wavelet Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Miguel A. Martínez-Prieto
    • 1
    • 2
  • Mario Arias Gallego
    • 1
    • 3
  • Javier D. Fernández
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversidad de ValladolidSpain
  2. 2.Department of Computer ScienceUniversidad de ChileChile
  3. 3.Digital Enterprise Research InstituteNational University of IrelandGalwayIreland

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