Title: Ontological Multidimensional Data Models and Contextual Data Qality,
Abstract: Data quality assessment and data cleaning are context-dependent activities.
Motivated by this observation, we propose the Ontological Multidimensional Data
Model (OMD model), which can be used to model and represent contexts as
logic-based ontologies. The data under assessment is mapped into the context,
for additional analysis, processing, and quality data extraction. The resulting
contexts allow for the representation of dimensions, and multidimensional data
quality assessment becomes possible. At the core of a multidimensional context
we include a generalized multidimensional data model and a Datalog+/- ontology
with provably good properties in terms of query answering. These main
components are used to represent dimension hierarchies, dimensional
constraints, dimensional rules, and define predicates for quality data
specification. Query answering relies upon and triggers navigation through
dimension hierarchies, and becomes the basic tool for the extraction of quality
data. The OMD model is interesting per se, beyond applications to data quality.
It allows for a logic-based, and computationally tractable representation of
multidimensional data, extending previous multidimensional data models with
additional expressive power and functionalities.