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 PARQL-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 PARQL-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 PARQL-ANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Magliacane, S., Bozzon, A., & Della Valle, E. (2012). Efficient execution of top-K SPARQL queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7649 LNCS, pp. 344–360). Springer Verlag. https://doi.org/10.1007/978-3-642-35176-1_22
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