The amount of available Linked Data on the Web is increasing, and data providers start to publish statistical datasets that comprise numerical data. Such statistical datasets differ significantly from the currently predominant networkstyle data published on the Web. We explore the possibility of integrating statistical data from multiple Linked Data sources. We provide a mapping from statistical Linked Data into the Multidimensional Model used in data warehouses. We use an extract-transform-load (ETL) pipeline to convert statistical Linked Data into a format suitable for loading into an open-source OLAP system, and thus demonstrate how standard OLAP infrastructure can be used for elaborate querying and visualisation of integrated statistical Linked Data. We discuss lessons learned from three experiments and identify areas which require future work to ultimately arrive at a well-interlinked set of statistical data from multiple sources which is processable with standard OLAP systems.

Abstract. Online Analytical Processing (OLAP) promises an interface to analyse Linked Data containing statistics going beyond other interaction paradigms such as follow-your-nose browsers, faceted-search interfaces and query builders. As a new way to interact with statistical Linked Data we define comon OLAP operations on data cubes modelled in RDF and show how a nested set of OLAP operations lead to an OLAP query. Then, we show how to transform an OLAP query to a SPARQL query which generates all required facts from the data cube. Both metadata and OLAP queries are issued directly on a triple store; therefore, if the RDF is modified or updated, changes are propagated directly to OLAP clients.

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.

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