Storing data according to the multidimensional model, in particular following star schemas, has demonstrated to be one of the most adequate forms to ease the exploration of data. However, this exploration has been limited to be query-based, leaving the discovery of hidden information to a second plan. The main reason for this, relates to the inability of traditional mining techniques to deal with several data tables at the same time. In this paper, we propose a new approach to mine patterns among data stored as a star schema, based in a domain driven framework, where available knowledge is represented in a domain ontology. Pattern mining is performed by an apriori-based algorithm - the D2Apriori, but more efficient algorithms are being implemented and tested, in order to solve performance issues related with the large amount of data stored in data warehouses.