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Data quality assessment and data cleaning are context
dependent activities. Starting from this observation, in previous work
a context model for the assessment of the quality of a database was
proposed. A context takes the form of a possibly virtual database or
a data integration system into which the database under assessment is
mapped, for additional analysis, processing, and quality data extraction.
In this work, we extend contexts with dimensions, and by doing so, multidimensional
data quality assessment becomes possible. At the core of
multidimensional contexts we find ontologies written as Datalog
with provably good properties in terms of query answering. We
use this language to represent dimension hierarchies, dimensional constraints,
dimensional rules, and specifying quality data. Query answering
relies on and triggers dimensional navigation, and becomes an important
tool for the extraction of quality data.