Titlebook: Data Quality Management with Semantic Technologies; Christian Fürber Book 2016 Springer Fachmedien Wiesbaden 2016 Data Quality Problems.Da

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https://doi.org/10.1057/9780230113442at. Moreover, we configured a wiki, called data requirements wiki, which contains standard forms to capture data requirements and to store them based on the elements of our ontology, the DQM vocabulary.

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Specification of Initial Requirements,QM), which shall be developed to support data quality management activities by the use of ontologies. We thereby apply the Design Science Research Methodology (DSRM, cf. Peffers et al., 2008) process as explained in section 2.4. We start with describing the required artifacts with a motivating scena

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Architecture of the Semantic Data Quality Management Framework (SDQM),component of SDQM’s architecture as illustrated in figure 26, namely (1) the data acquisition layer, (2) the data storage layer, (3) the data quality management vocabulary (DQM Vocabulary), (4) the data requirements editor, and (5) the reporting layer. The design of the architecture is based on the

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Evaluation of the Semantic Data Quality Management Framework (SDQM), with the evaluation of precision and recall of SDQM’s data quality monitoring and assessment algorithms. The second part evaluates the practical applicability of SDQM by applying the framework to three different use cases, namely one business use case on material master data of a large organization