Temporal Representation for Scientific Data Provenance

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2012-09

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https://hdl.handle.net/2022/14665

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.

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provenance representation, logical clock, temporal data mining

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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.

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