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No Size Fits All – Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views




Published: 2013 Mai

Buchtitel: ESWC 2013, LNCS 7882
Seiten: 290-304
Verlag: Springer
Erscheinungsort: Heidelberg
Referierte Veröffentlichung
BibTeX

Kurzfassung
Statistics published as Linked Data promise efficient extrac- tion, transformation and loading (ETL) into a database for decision sup- port. 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 argu- ment for the need of such an engine, we first compare the performance of our generated SPARQL and of ROLAP SQL queries. Second, we measure the performance gain of RDF aggregate views that, similar to aggregate tables in ROLAP, materialise parts of the data cube.

Download: Media:Kaempgen harth ssb-benchmark ESWC-2013 own version.pdf

Projekt

LD-CubesSFB/Transregio 125SMART



Forschungsgruppe

Wissensmanagement