Ontological Multidimensional Data Models and Contextual Data Qality
This work addresses data quality assessment for data management systems, offering a novel approach but is incremental in extending existing multidimensional models.
The paper tackles the problem of context-dependent data quality assessment by proposing the Ontological Multidimensional Data Model (OMD model), which uses logic-based ontologies to represent contexts and enable multidimensional data quality assessment, resulting in a computationally tractable representation that extends previous models with enhanced expressive power.
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.