Title: How to implement multidimensional security into OLAP tools

Authors: Carlos Blanco, Eduardo Fernandez-Medina, Juan Trujillo, Mario Piattini

Addresses: Department of Information Technologies and Systems, Escuela Superior de Informatica, Alarcos Research Group – Institute of Information Technologies and Systems, University of Castilla-La Mancha, Paseo de la Universidad, 4. 13071, Ciudad Real, Spain. ' Department of Information Technologies and Systems, Escuela Superior de Informatica, Alarcos Research Group – Institute of Information Technologies and Systems, University of Castilla-La Mancha, Paseo de la Universidad, 4. 13071, Ciudad Real, Spain. ' Department of Software and Computing Systems, LUCENTIA Research Group, University of Alicante, San Vicente s/n, 03690, Alicante, Spain. ' Department of Information Technologies and Systems, Escuela Superior de Informatica, Alarcos Research Group – Institute of Information Technologies and Systems, University of Castilla-La Mancha, Paseo de la Universidad, 4. 13071, Ciudad Real, Spain

Abstract: Data Warehouses (DWs) manage historical information for the decision-making process and for enterprises. Online Analytical Processing Applications (OLAP) tools are the most used tools for implementing and consulting DWs and it is necessary to define security measures to avoid the accessing of unauthorised information by users by executing queries. It is vitally important to consider security requirements from the earliest stages of the development process. We have created a Model-Driven Architecture (MDA) to develop a secure DW and in this paper, we propose how to implement the security measures that are defined at upper abstraction levels using our approach to SQL Server Analysis Services (SSAS).

Keywords: multidimensional modelling; security; online analytical processing applications; OLAP tools; SQL Server Analysis Services; SSAS; data warehouses; unauthorised access; model-driven architecture.

DOI: 10.1504/IJBIDM.2008.022136

International Journal of Business Intelligence and Data Mining, 2008 Vol.3 No.3, pp.255 - 276

Available online: 19 Dec 2008 *

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