A multidimensional data model with subcategories for flexibly capturing summarizability

Abstract

In multidimensional (MD) databases and data warehouses we commonly prefer instances that have summarizable dimensions. This is because they have good properties for query answering. Most typically, with summarizable dimensions, precomputed and materialized aggregate query results at lower levels of the dimension hierarchy can be used to correctly compute results at higher levels of the same hierarchy, improving efficiency. Being summarizability such a desirable property, we argue that some established MD models cannot properly model the summarizability condition, and this is a consequence of the limited expressive power of the modeling languages. We propose an extension to the Hurtado-Meldelzon (HM) MD model with subcategories, the EHM model, and show that it allows to capture the summarizability. We propose an efficient algorithm that, for a given cube view (i.e. MD aggregate query) in an EHM database, determines from which minimal subset of precomputed cube views it can be correctly computed. Finally, we show how the EHM can be implemented with minor modifications to the familiar ROLAP schemas.