Paper Info
Title
Optimizing SPARQL-to-SQL Rewriting
Abstract
The vast majority of the structured data of our age is stored in relational databases. In order to link and integrate this data on the Web, it is of paramount importance to map relational data to the RDF data model and make Linked Data interfaces to the data available. We can distinguish two main approaches: First, the database can be transformed into RDF row by row and the resulting knowledge base can be exposed using a triple store. Second, an RDB2RDF mapper performs SPARQL-to-SQL rewriting and thus exposes a virtual RDF graph based on the relational database. The key challenge of such a SPARQL-to-SQL rewriting is to create a SQL query which can be efficiently executed by the optimizer of the underlying relational database. In this article we discuss and evaluate the impact of different optimizations on query execution time using SparqlMap, a R2RML compliant SPARQL-to-SQL rewriter and compare the performance with state-of-the-art systems.
Year
DOI
Venue
2013
10.1145/2539150.2539247
iiWAS
Keywords
Field
DocType
rdf data model,relational data,virtual rdf graph,rdf row,r2rml compliant sparql-to-sql rewriter,relational databases,relational database,optimizing sparql-to-sql rewriting,structured data,sql query,underlying relational database,sql,sparql
SQL,Data mining,RDF query language,Relational database,Database model,Computer science,Data definition language,View,Database design,SPARQL,Database
Conference
Citations 
PageRank 
References 
1
0.36
6
Authors
3
Name
Order
Citations
PageRank
Jörg Unbehauen1686.85
Claus Stadler236326.65
Sören Auer35711418.56