FedX: Optimization Techniques for Federated Query Processing on Linked Data

  • Andreas Schwarte
  • Peter Haase
  • Katja Hose
  • Ralf Schenkel
  • Michael Schmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)


Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on the Web, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines.


Query Processing Service Description Conjunctive Query SPARQL Query Source Selection 
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 2011

Authors and Affiliations

  • Andreas Schwarte
    • 1
  • Peter Haase
    • 1
  • Katja Hose
    • 2
  • Ralf Schenkel
    • 2
  • Michael Schmidt
    • 1
  1. 1.Fluid Operations AGWalldorfGermany
  2. 2.Max-Planck Institute for InformaticsSaarbrückenGermany

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