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.

Original languageEnglish
Title of host publicationThe Semantic Web, ISWC 2012
Subtitle of host publication11th International Semantic Web Conference, Proceedings
Pages344-360
Number of pages17
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event11th International Semantic Web Conference, ISWC 2012 - Boston, MA, United States
Duration: 11 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7649 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference11th International Semantic Web Conference, ISWC 2012
CountryUnited States
CityBoston, MA
Period11/11/1215/11/12

ID: 33899065