  The business intelligence and decision-support systems used in many
application domains casually rely on data warehouses, which are
decision-oriented data repositories modeled as multidimensional (MD)
structures. MD structures help navigate data through hierarchical levels of
detail. In many real-world situations, hierarchies in MD models are complex,
which causes data aggregation issues, collectively known as the summarizability
problem. This problem leads to incorrect analyses and critically affects
decision making. To enforce summarizability, existing approaches alter either
MD models or data, and must be applied a priori, on a case-by-case basis, by an
expert. To alter neither models nor data, a few query-time approaches have been
proposed recently, but they only detect summarizability issues without solving
them. Thus, we propose in this paper a novel approach that automatically
detects and processes summarizability issues at query time, without requiring
any particular expertise from the user. Moreover, while most existing
approaches are based on the relational model, our approach focus on an XML MD
model, since XML data is customarily used to represent business data and its
format better copes with complex hierarchies than the relational model.
Finally, our experiments show that our method is likely to scale better than a
reference approach for addressing the summarizability problem in the MD
context.
