Multidimensional analysis allows decision makers to effici-ently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data ware-house such as facts and dimension hierarchies to explore the information and aggregate it at different levels of detail in an accurate way. A conceptual model of such MD structures serves as abstract basis of the subsequent implementation according to one specific technology. However, there is a se-mantic gap between a conceptual model and its implemen-tation which complicates an adequate treatment of summa-rizability issues, which in turn may lead to erroneous results of data analysis tools and cause the failure of the whole data warehouse project. To bridge this gap for relationships be-tween facts and dimension, we present an approach at the conceptual level for (i) identifying problematic situations in fact-dimension relationships, (ii) defining these relationships in a conceptual MD model, and (iii) applying a normaliza-tion process to transform this conceptual MD model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we also describe our Eclipse-based implementation of this normalization process
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