No Size Fits All - Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views
author: Benedikt Kämpgen, Institute of Applied Informatics and Formal Description Methods (AIFB), Karlsruhe Institute of Technology (KIT)
published: July 8, 2013, recorded: May 2013, views: 3825
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 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 slides: eswc2013_kaempgen_views_01.pdf (1013.7 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Write your own review or comment: