As with most consolidated analysis techniques, online analytical processing (OLAP) usually assumes a local data environment (for example, analysis within an organization) in which multidimensional datasets are modeled according to proprietary standards and formats.

On the other hand, emerging trends are bringing more and more publicly available data onto the web, defining one of the next challenges for data analysis techniques: processing open data like local datasets. In this context, semantic technology plays a key and critical role, underpinning initiatives--such as linked data--aimed at pushing data interoperability throughout the web. According to this philosophy, many statistical datasets are currently published using the resource description framework (RDF) data cube vocabulary (QB), the current World Wide Web Consortium (W3C) standard.

In this paper, the authors propose an effective technique for applying OLAP in the context of open data. Indeed, QB is not able to fully support OLAP due to several issues in terms of structure, as well as in terms of support for aggregate functions and descriptive attributes. This gap is covered by QB4OLAP, which extends QB with the necessary constructs and the rationale to be fully compliant with OLAP. Furthermore, the authors provide a semiautomatic method for the enrichment of an existing QB dataset with the QB4OLAP semantics.

I enjoyed reading this interesting, clear, and well-structured paper. I found it informative and inspiring.