Authors: Jose Zubcoff, Juan Trujillo
Tags: 2006, conceptual modeling
By using data mining techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncov-ering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish theconceptual design of DWs by following the multidimensional (MD) modeling paradigm. In previous work, we have pro-posed a UML profile for DWs enabling the specification of main MD properties at conceptual level. This paper presents anovel approach to integrating data mining models into multidimensional models in order to accomplish the conceptualdesign of DWs with Association Rules (AR). To this goal, we extend our previous work by providing another UML profilethat allows us to specify Association Rules mining models for DW at conceptual level in a clear and expressive way. Themain advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Ware-house, instead of the traditional method of specifying Association Rules by considering only the final database implemen-tation structures such as tables, rows or columns. In this way, ARs are specified in the early stages of a DW project, thusreducing the development time and cost. Finally, in order to show the benefits of our approach, we have implemented thespecified Association Rules on a commercial database management serverRead the full paper here: https://reader.elsevier.com/reader/sd/pii/S0169023X06001911?token=9A6AD739329A844E9004281EAE4CFA8AED4FED3AE004995F0B6A88B7CDD0168219F314C26B4A1D0FDADE5821ABD775BC
