Hybrid SPARQL queries: fresh vs. fast results

  • Authors:

  • Jürgen Umbrich;Marcel Karnstedt;Aidan Hogan;Josiane Xavier Parreira

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland

  • Venue:
  • ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
  • Year:
  • 2012

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Abstract

For Linked Data query engines, there are inherent trade-offs between centralised approaches that can efficiently answer queries over data cached from parts of the Web, and live decentralised approaches that can provide fresher results over the entire Web at the cost of slower response times. Herein, we propose a hybrid query execution approach that returns fresher results from a broader range of sources vs. the centralised scenario, while speeding up results vs. the live scenario. We first compare results from two public SPARQL stores against current versions of the Linked Data sources they cache; results are often missing or out-of-date. We thus propose using coherence estimates to split a query into a sub-query for which the cached data have good fresh coverage, and a sub-query that should instead be run live. Finally, we evaluate different hybrid query plans and split positions in a real-world setup. Our results show that hybrid query execution can improve freshness vs. fully cached results while reducing the time taken vs. fully live execution.