A survey on summarizability issues in multidimensional modeling
The development of a data warehouse (DW) system is based on a conceptual multidimensional model, which provides a high level of abstraction in accurately and expressively describing real-world situations. Once this model is designed, the corresponding logical representation must be obtained as the basis of the implementation of the DW according to one specific technology. However, even though a good conceptual multidimensional model is designed underneath a DW, there is a semantic gap between this model and its logical representation. In particular, this gap complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools. Research addressing this topic has produced only partial solutions, and individual terminology used by different parties hinders further progress. Consequently, based on a unifying vocabulary, this survey sheds light on (i) the weak and strong points of current approaches for modeling complex multidimensional structures that reflect real-world situations in a conceptual multidimensional model and (ii) existing mechanisms to avoid summarizability problems when conceptual multidimensional models are being implemented.
summarizability multidimensional modeling data warehouse data analysis