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dc.contributor.author Cheah, You-Wei
dc.contributor.author Plale, Beth
dc.date.accessioned 2012-10-22T13:24:45Z
dc.date.available 2012-10-22T13:24:45Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/2022/14744
dc.description.abstract 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. en_US
dc.language.iso en_US en_US
dc.subject Data Provenance en_US
dc.subject Provenance Quality en_US
dc.subject Scientific Workflows en_US
dc.subject Provenance Analysis en_US
dc.title Provenance Analysis: Towards Quality Provenance en_US
dc.altmetrics.display true en_US


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