Efficient Execution of Top-K SPARQL Queries

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.