Description
Traditionally, data used in OLAP (online analytical processing) have been limited to the contents of the data warehouse of a company. However, the needs for analysis are often more demanding and data are needed from different sources. In this article, we study how the semantics of data sources can be described to allow combining data from several sources into an OLAP cube. We apply Semantic Web technologies for defining an OWL/RDF ontology for OLAP data sources and OLAP cubes. These definitions are then utilised in OLAP cube formation by posing an OWL/RDF ontology-based query against them. We use Grid technologies to enhance the efficiency of processing and ensuring security. Our primary interest is in the cube construction (i.e., ETL process), and we assume that standard OLAP methods can be used for the actual analysis. Our tests show that the proposed approach can speed up the construction of an OLAP cube for ad hoc queries by supporting a high-level query language and reducing the amount of required data.