  In spite of its fundamental importance, inference has not been an inherent
function of multidimensional models and analytical applications. These models
are mainly aimed at numeric (quantitative) analysis where the notions of
inference and semantics are not well defined. In this paper we argue that
inference can be and should be integral part of multidimensional data models
and analytical applications. It is demonstrated how inference can be defined
using only multidimensional terms like axes and coordinates as opposed to using
logic-based approaches. We propose a novel approach to inference in
multidimensional space based on the concept-oriented model of data and
introduce elementary operations which are then used to define constraint
propagation and inference procedures. We describe a query language with
inference operator and demonstrate its usefulness in solving complex analytical
tasks.
