Description

Research Collaboration: QB4OLAP Enrichment of QB Data sets

This collaboration aims at enabling full-fledge OLAP-like analysis of Statistical Linked Open Data published on the Web. 

At the moment, Statistical Linked Open Data are published using the RDF Data Cube Vocabulary (QB). However, QB lacks some essential metadata concepts (e.g., dimension levels representing data granularity) to support OLAP operations that typically aggregate data accordingly. The QB4OLAP vocabulary was proposed to extend QB and introduce the lacking metadata concepts for supporting OLAP. Thus, in this collaboration, we address the challenge of enriching existing QB data sets with these additional QB4OLAP metadata concepts.

Goals

• Detailed elaboration on the advantages of QB4OLAP over QB for OLAP-like analysis.
• Definition of a methodology for enriching existing QB data sets with QB4OLAP.
• Maximal possible automation of the enrichment process.
• Development of the related tools.

Related publications

2016
Jovan Varga, Alejandro A. Vaisman, Oscar Romero, Lorena Etcheverry, Torben Bach Pedersen, Christian Thomsen: Dimensional enrichment of statistical linked open data. J. Web Semant. 2016
Jovan Varga, Lorena Etcheverry, Alejandro A. Vaisman, Oscar Romero, Torben Bach Pedersen, Christian Thomsen: QB2OLAP: Enabling OLAP on Statistical Linked Open Data. ICDE 2016
Amine Ghrab, Oscar Romero, Sabri Skhiri, Alejandro A. Vaisman, Esteban Zimányi: GRAD: On Graph Database Modeling. CoRR 2016

2015
Amine Ghrab, Oscar Romero, Sabri Skhiri, Alejandro A. Vaisman, Esteban Zimányi: A Framework for Building OLAP Cubes on Graphs. ADBIS 2015

2013
Alberto Abelló, Jérôme Darmont, Lorena Etcheverry, Matteo Golfarelli, Jose-Norberto Mazón, Felix Naumann, Torben Bach Pedersen, Stefano Rizzi, Juan Trujillo, Panos Vassiliadis, Gottfried Vossen: Fusion Cubes: Towards Self-Service Business Intelligence. IJDWM 2013