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In recent years, intense usage of computing has been the main strategy of investigations in several scientific research projects. The progress in computing technology has opened unprecedented opportunities for systematic collection of experimental data and the associated analysis that were considered impossible only few years ago.

This paper focuses on the strategies in use: it reviews the various components that are necessary for an effective solution that ensures the storage, the long term preservation, and the worldwide distribution of large quantities of data that are necessary in a large scientific research project.

The paper also mentions several examples of data management solutions used in High Energy Physics for the CERN Large Hadron Collider (LHC) experiments in Geneva, Switzerland which generate more than 30,000 terabytes of data every year that need to be preserved, analyzed, and made available to a community of several tenth of thousands scientists worldwide.

Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are best-practice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs.