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- 204 creator andreas-harth.
- 204 creator benedikt-kaempgen.
- 204 type InProceedings.
- 204 label "No Size Fits All -- Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views".
- 204 sameAs 204.
- 204 abstract "Statistics published as Linked Data promise efficient extraction, transformation and loading (ETL) into a database for decision support. The predominant way to implement analytical query capabilities in industry are specialised engines that translate OLAP queries to SQL queries on a relational database using a star schema (ROLAP). A more direct approach than ROLAP is to load Statistical Linked Data into an RDF store and to answer OLAP queries using SPARQL. However, we assume that general-purpose triple stores -- just as typical relational databases -- are no perfect fit for analytical workloads and need to be complemented by OLAP-to-SPARQL engines. To give an empirical argument for the need of such an engine, we first compare our generated SPARQL to ROLAP SQL queries in terms of performance. Second, we measure the performance gain of RDF aggregate views that, similar to aggregate tables in ROLAP, materialise part of the data cube.".
- 204 hasAuthorList authorList.
- 204 isPartOf proceedings.
- 204 keyword "Aggregate Views".
- 204 keyword "Linked Data".
- 204 keyword "OLAP".
- 204 keyword "Star Schema Benchmark".
- 204 title "No Size Fits All -- Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views".