Return to Projects

WaRG: Warehousing RDF Graphs

WaRG is a warehouse-style analytics platform on RDF graphs. The tool stores data in kdb+ with a Java frontend based on the Prefuse Visualization toolkit.

The novelty of WaRG is to redesign the full stack of Data Warehouse abstractions and tools for heterogeneous, semantics-rich RDF data; this enables a WaRG RDF DW to be an RDF graph itself, heterogeneous and semantics-rich in its turn. Thus, WaRG benefits both from powerful analytics and the rich interoperability and semantic features of Semantic Web databases.



Efficient OLAP Operations For RDF Analytics Elham Akbari-Azirani, François Goasdoué, Ioana Manolescu, Alexandra Roatis, International Workshop on Data Engineering meets the Semantic Web (DESWeb), Apr 2015, Seoul, South Korea. <>. <10.1109/ICDEW.2015.7129548> 

Efficient OLAP Operations for RDF Analytics Elham Akbari Azirani, François Goasdoué, Ioana Manolescu, Alexandra Roatis. Inria research report no. 8668, OAK team, Inria Saclay INRIA, 2015 (extended version of the DESWeb article)

RDF Analytics: Lenses over Semantic Graphs, Dario Colazzo, François Goasdoué, Ioana Manolescu, Alexandra Roatiș, 23rd International World Wide Web Conference, April 2014, Seoul, Korea

Analysing RDF Data: A Realm of New Possibilities, Alexandra Roatiș, ERCIM News No. 96 published – special theme: “Linked Open Data”, January 2014

Warehousing RDF Graphs, Dario Colazzo, François Goasdoué, Ioana Manolescu, Alexandra Roatiș, Bases de Données Avancées, October 2013, Nantes, France.

We present below scenarios for rendering WaRG on two sample Analytical Schemas.

  •     Example One:- We present below an abridged version of Person schema adapted from DBpedia. The schema  holds data concerning persons connected to the software industry. For instance, a dbpo:Person=Anon working in dbpo:Organisation=Scroogle is a dbpo:creator of dbpo:software=Scrooge.

smallDemoBDA_2smallDemoBDA_3 smallDemoBDA_4 smallDemoBDA_5

  •    Example Two:- The schema below holds more information about a Person than in Example 1. Here, dbpo:Person is  more generic in the sense that the Person instances reflected, do not have to belong to a single industry as was the case above. Sample tuples here can state, for instance, that dbpo:Person=Anne has the dbpo:EthnicGroup=French, is from dbpo:PopulatedPlace=Amiens, is associated with dbpo:EducationalInstitution=UniversiteParisSud and is rewarded with dbpo:Award=ACMTuring2013.

bigDemoBDA_2 bigDemoBDA_3 bigDemoBDA_4 bigDemoBDA_5

Permanent link to this article: