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Please cite this article as: C. Blanco, I.G. de Guzmán, E. Fernández-Medina, J. Trujillo, An architecture for automatically developing Secure OLAP applications from models, Information and Software Technology (2014), doi: http://dx.doi.org/10.1016/j.infsof. 2014.10.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractDecision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses. In previous work we have defined a ModelDriven based approach for developing a secure DWs repository by following a relational approach. Nevertheless, is also important to define security constraints in the metadata layer that connects the DWs repository with the OLAP tools, that is, over the same multidimensional structures that final users manage. This paper incorporates a proposal to develop secure OLAP applications into our previous approach: improves a UML profile for conceptual modelling; defines a logical metamodel for OLAP applications; and * Corresponding author Email addresses: Carlos.Blanco@unican.es (Carlos Blanco), Ignacio.GRodriguez@uclm.es (Ignacio García-Rodríguez de Guzmán), Eduardo.Fdezmedina@uclm.es (Eduardo Fernández-Medina), jtrujillo@dlsi.ua.es (Juan Trujillo) Preprint submitted to Information and Software Technology November 4, 2014 defines and implements transformations from conceptual to logical models, and from logical models to the secure implementation into a specific OLAP tool (SQL Server Analysis Services).

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Cloud computing offers massive scalability and elasticity required by many scientific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new opportunities for application developers. This paper investigates how workflow systems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).

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