Towards a Quality Model for Effective Data Selection in Collaboratories
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Managing information quality in e-science: the qurator workbench
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Lifecycle of Scientific Workflows and their Provenance: A Usage Perspective
SERVICES '08 Proceedings of the 2008 IEEE Congress on Services - Part I
Reasoning on Scientific Workflows
SERVICES '09 Proceedings of the 2009 Congress on Services - I
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Data quality is an important component of modern scientific discovery. Many scientific discovery processes consume data from a diverse array of resources such as streaming sensor networks, web services, and databases. The validity of a scientific computation's results is highly dependent on the quality of these input data. Scientific workflow systems are being increasingly used to automate scientific computations by facilitating experiment design, data capture, integration, processing, and analysis. These workflows may execute in grid or cloud environments, and if the data produced during workflow execution is deemed unusable or low in quality, execution should stop to prevent wasting these valuable resources. We propose an approach in the Kepler scientific workflow system for monitoring data quality and demonstrate its use for oceanography and bioinformatics domains.