Rewritting Data Cleaning Operations Defined at a Conceptual Level

The handling of increasing amounts of data creates the need to deal with redundant and/or complementary repositories which are disparate in their data models and/or their data structures. Current data cleaning techniques developed to tackle data quality problems are just suitable for scenarios were all repositories share the same model and structure. Recently, a novel methodology was proposed to overcome this limitation. In the context of this methodology, it is outlined a generic process aimed to rewrite Data Cleaning Operation (DCO) specified at a conceptual level for a given domain ontology, and whose structure and semantics are described by the E-DQM ontology, according to another data structure of a target repository and another vocabulary required by an existing Data Cleaning Tool. The proposed algorithm includes validation of the rewritten DCOs and its serialization to the required output format.