SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th international conference on World Wide Web
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Probe Minimization by Schedule Optimization: Supporting Top-K Queries with Expensive Predicates
IEEE Transactions on Knowledge and Data Engineering
Sum-max monotonic ranked joins for evaluating top-k twig queries on weighted data graphs
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
SPARQL basic graph pattern optimization using selectivity estimation
Proceedings of the 17th international conference on World Wide Web
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Semantics and complexity of SPARQL
ACM Transactions on Database Systems (TODS)
Optimal algorithms for evaluating rank joins in database systems
ACM Transactions on Database Systems (TODS)
Foundations of SPARQL query optimization
Proceedings of the 13th International Conference on Database Theory
f-SPARQL: a flexible extension of SPARQL
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
AnQL: SPARQLing up annotated RDFS
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Querying the semantic web with preferences
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Top-k retrieval for ontology mediated access to relational databases
Information Sciences: an International Journal
Top-k linked data query processing
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Cost-Aware Rank Join with Random and Sorted Access
IEEE Transactions on Knowledge and Data Engineering
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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.