种源分析:走向优质种源

Y. Cheah, Beth Plale
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引用次数: 28

摘要

数据来源是描述数据产品生命周期的元数据的一个关键部分,对于帮助科学家更好地理解和促进科学结果的可再现性和重用至关重要。来源收集系统经常动态捕获来源,应用程序和来源工具之间的协议可能不可靠。因此,数据来源可能变得含糊不清或根本不准确。在本文中,我们确定了数据来源中可能存在的质量问题。我们还建立了关键的质量维度,这对评估来源质量尤其重要。我们基于这些质量维度分析了合成种源和真实种源,并总结了我们对种源质量的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Provenance analysis: Towards quality provenance
Data provenance, a key piece of metadata that describes the lifecycle of a data product, is crucial in aiding scientists to better understand and facilitate reproducibility and reuse of scientific results. Provenance collection systems often capture provenance on the fly and the protocol between application and provenance tool may not be reliable. As a result, data provenance can become ambiguous or simply inaccurate. In this paper, we identify likely quality issues in data provenance. We also establish crucial quality dimensions that are especially critical for the evaluation of provenance quality. We analyze synthetic and real-world provenance based on these quality dimensions and summarize our contributions to provenance quality.
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