Publishing OLAP Cubes on the Semantic Web

  • Alejandro VaismanEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 253)


The availability of large repositories of semantically annotated data on the web is opening new opportunities for enhancing Decision-Support Systems. In addition, the advent of initiatives such as Open Data and Open Government, together with the Linked Open Data paradigm, are promoting publication and sharing of multidimensional data (MD) on the web. In this paper we address the problem of representing MD data using Semantic Web (SW) standards. We discuss how MD data can be represented and queried directly over the SW, without the need to download data sets into local data warehouses. We first comment on the RDF Data Cube Vocabulary (QB), the current W3C recommendation, and show that it is not enough to appropriately represent and query MD data on the web. In order to be able to support useful Online Analytical Process (OLAP) analysis, extension to QB, denoted QB4OLAP, has been proposed. We provide an in-depth comparison between these two proposals, and show that extending QB with QB4OLAP can be done without re-writing the observations, (the largest part of a QB data set). We provide extensive examples of the QB4OLAP representation, using a portion of the Eurostat data set and the well-known Northwind database. Finally, we present a high-level query language, called QL, that allows OLAP users not familiar with SW concepts or languages, to write and execute OLAP operators without any knowledge of RDF or SPARQL, the standard data model and query language, respectively, for the SW. QL queries are automatically translated into SPARQL (using the QB4OLAP metadata) and executed over an endpoint.


Data warehousing OLAP Semantic web RDF SPARQL Linked data 



The author is partially funded by the project PICT 2014 - 0787 CC 0800082612, awarded by the Argentinian Scientific Agency.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Instituto Tecnológico de Buenos AiresBuenos AiresArgentina

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