The web is changing the way in which data warehouses are designed, used, and queried. With the advent of initiatives such as Open Data and Open Government, organizations want to share their multidimensional data cubes and make them available to be queried online. The RDF data cube vocabulary (QB), the W3C standard to publish statistical data in RDF, presents several limitations to fully support the multidimensional model. The QB4OLAP vocabulary extends QB to overcome these limitations, allowing to im- plement the typical OLAP operations, such as rollup, slice, dice, and drill-across using standard SPARQL queries. In this paper we introduce a formal data model where the main object is the data cube, and define OLAP operations using this model, independent of the underlying representation of the cube. We show then that a cube expressed using our model can be represented using the QB4OLAP vocabulary, and finally we provide a SPARQL implementation of OLAP operations over data cubes in QB4OLAP.
In @cite_4 the authors present a framework for Exploratory OLAP over Linked Open Data sources, where the MD schema of the data cube is expressed in QB4OLAP and VoID. Based on this MD schema the system is able to query data sources, extract and aggregate data, and build an OLAP cube. The MD information retrieved from external sources is also stored using QB4OLAP.
@cite_4
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Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF), and BI tools have a huge potential of achieving better results by integrating real-time data from web sources into the analysis process. In this paper, we describe a framework for so-called exploratory OLAP over RDF sources. We propose a system that uses a multidimensional schema of the OLAP cube expressed in RDF vocabularies. Based on this information the system is able to query data sources, extract and aggregate data, and build a cube. We also propose a computer-aided process for discovering previously unknown data sources and building a multidimensional schema of the cube. We present a use case to demonstrate the applicability of the approach.