Quality views: capturing and exploiting the user perspective on data quality
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Understanding provenance black boxes
Distributed and Parallel Databases
Monitoring data quality in Kepler
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Quality assessment of MAGE-ML genomic datasets using DescribeX
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
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Data-intensive e-science applications often rely on third-party data found in public repositories, whose quality is largely unknown. Although scientists are aware that this uncertainty may lead to incorrect scientific conclusions, in the absence of a quantitative characterization of data quality properties they find it difficult to formulate precise data acceptability criteria. We present an Information Quality management workbench, called Qurator, that supports data experts in the specification of personal quality models, and lets them derive effective criteria for data acceptability. The demo of our working prototype will illustrate our approach on a real e-science workflow for a bioinformatics application.