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RDFMatView: Indexing RDF data using Materialized SPARQL Queries (2010)
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| Venue: | In International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS |
| Citations: | 1 - 0 self |
BibTeX
@INPROCEEDINGS{Castillo10rdfmatview:indexing,
author = {Roger Castillo and Christian Rothe and Ulf Leser},
title = {RDFMatView: Indexing RDF data using Materialized SPARQL Queries},
booktitle = {In International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS},
year = {2010}
}
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Abstract
Abstract. The Semantic Web aims to create a universal medium for the exchange of semantically tagged data. The idea of representing and querying this information by means of directed labelled graphs, i.e., RDF and SPARQL, has been widely accepted by the scientific community. However, even when most current implementations of RDF/SPARQL are based on ad-hoc storage systems, processing complex queries on large data sets incurs a high number of joins, which may slow down performance. In this article we propose materialized SPARQL queries as indexes on RDF data sets to reduce the number of necessary joins and thus query processing time. We provide a formal definition of materialized SPARQL queries, a cost model to evaluate their impact on query performance, a storage scheme for the materialization, and an algorithm to find the optimal set of indexes given a query. We also present and evaluate different approaches to integrate materialized queries into an existing SPARQL query engine. An evaluation shows that our approach can drastically decrease the query processing time compared to a direct evaluation.
Keyphrases
materialized sparql query indexing rdf data sparql query engine high number complex query query processing time rdf sparql large data set processing time ad-hoc storage system formal definition rdf data set optimal set materialized query universal medium cost model sparql query direct evaluation necessary join current implementation storage scheme evaluate different approach query performance semantic web aim labelled graph scientific community
