OntoDW: An Approach for Extraction of Conceptualizations from Data Warehouses

Business Intelligence (BI) fosters proper decision-making in organizations, mainly by providing the means to analyze historical data stored in repositories called Data Warehouses (DW). However, formal representation of which concepts are implemented in a DW rarely exists, which would be important to clarify and semantically describe the domain concepts behind the data stored in a DW, as well as the analytical concepts that are available for the BI tools. Examples of important pieces of knowledge that are frequently hidden into the DW are: which domain concepts are available as analysis perspectives (dimensions), how the domain concepts relate to each other, which metrics (facts) are available and what do they mean, which domain perspectives are considered for each metric and how metrics may be aggregated. On the other hand, one of the relevant uses of an ontology for the Computer Science area is as a codified artifact that formally represents a shared conceptualization about a universe of discourse. Therefore, ontologies can be used to represent both domain and analytical concepts codified and stored in a DW. However, extracting these concepts from an already-inproduction DW is not a trivial task, especially in medium and large organizations, often with tens of metrics and tens (even hundreds) of dimensions and potential aggregations. In this paper, we define a set of mapping rules from DW constructs to conceptual elements (concepts and relationships), towards automatically extracting an ontology codified in OWL. The proposal was successfully evaluated in a real scenario of a Brazilian financial institution.