Solving summarizability problems in fact-dimension relationships for multidimensional models

Multidimensional analysis allows decision makers to efficiently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data warehouse 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 semantic gap between a conceptual model and its implementation which complicates an adequate treatment of summarizability 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 between 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 normalization process to transform this conceptual MD model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we also describe our Eclipsebased implementation of this normalization process.