面向协作实验室有效数据选择的质量模型

22nd International Conference on Data Engineering Workshops (ICDEW'06) Pub Date : 2006-04-03 DOI:10.1109/ICDEW.2006.150

Yogesh L. Simmhan, Beth Plale, Dennis Gannon

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引用次数: 31

摘要

数据驱动的科学应用程序利用工作流框架来执行复杂的数据流，从而产生质量未知的派生数据产品。我们讨论了我们正在进行的关于质量模型的研究，该模型为用户提供了对数据质量的综合估计，该模型可以根据用户的应用需求进行调整，并且可以作为数字质量分数，从而实现数据集的统一比较，为社区提供了一种信任派生数据的方法。

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Towards a Quality Model for Effective Data Selection in Collaboratories

Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data.

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22nd International Conference on Data Engineering Workshops (ICDEW'06)

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