Efficient Execution of Top-K SPARQL Queries

  • Sara Magliacane
  • Alessandro Bozzon
  • Emanuele Della Valle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)


Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The \(\mathcal{S}\)PARQL-\(\mathcal{R}\)ANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for \(\mathcal{S}\)PARQL-\(\mathcal{R}\)ANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a \(\mathcal{S}\)PARQL-\(\mathcal{R}\)ANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.


Random Access Rank Operator Graph Pattern Execution Plan Query Optimization 
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

  • Sara Magliacane
    • 1
    • 2
  • Alessandro Bozzon
    • 1
  • Emanuele Della Valle
    • 1
  1. 1.Politecnico of MilanoMilanoItaly
  2. 2.VU University AmsterdamThe Netherlands

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