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The authors present a Heterogenous Data Quality Methodology (HDQM) for Data Quality (DQ) assessment and improvement that considers all types of data managed in an organization, namely structured data represented in databases, semistructured data usually represented in XML, and unstructured data represented in documents. They also define a meta-model in order to describe the relevant knowledge managed in the methodology. The different types of data are translated in a common conceptual representation. They consider two dimensions widely analyzed in the specialist literature and used in practice: Accuracy and Currency. The methodology provides stakeholders involved in DQ management with a complete set of phases for data quality assessment and improvement. A non trivial case study from the business domain is used to illustrate and validate the methodology.
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