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 are 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, and dimensional rules and define predicates for quality data specification. Query answering relies on 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.
Funder
NSERC Discovery
NSERC Strategic Network on Business Intelligence
Publisher
Association for Computing Machinery (ACM)
Subject
Information Systems and Management,Information Systems
Cited by
10 articles.
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