Temporal Representation for Scientific Data Provenance

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dc.contributor.author	Chen, Peng
dc.contributor.author	Plale, Beth
dc.contributor.author	Aktas, Mehmet S.
dc.date.accessioned	2012-09-14T20:11:54Z
dc.date.available	2012-09-14T20:11:54Z
dc.date.issued	2012-09
dc.identifier.citation	Chen, Peng, Beth Plale, and Mehmet Aktas. “Temporal Representation for Scientific Data Provenance.” Preprint of paper accepted for the 8th IEEE International Conference on eScience (eScience 2012), submitted September 14, 2012. http://hdl.handle.net/2022/14665.	en
dc.identifier.uri	http://hdl.handle.net/2022/14665
dc.description.abstract	Provenance of digital scientific data is an important piece of the metadata of a data object. It can however grow voluminous quickly because the granularity level of capture can be high. It can also be quite feature rich. We propose a representation of the provenance data based on logical time that reduces the feature space. Creating time and frequency domain representations of the provenance, we apply clustering, classification and association rule mining to the abstract representations to determine the usefulness of the temporal representation. We evaluate the temporal representation using an existing 10 GB database of provenance captured from a range of scientific workflows.	en
dc.description.sponsorship	NASA grant NNX10AM03G	en
dc.language.iso	en_US	en
dc.publisher		en
dc.subject	provenance representation	en
dc.subject	logical clock	en
dc.subject	temporal data mining	en
dc.title	Temporal Representation for Scientific Data Provenance	en
dc.type	Preprint	en
dc.altmetrics.display	true	en_US