Enhancing and abstracting scientific workflow provenance for data publishing

Date

2013-03-22

Author

Pınar, Alper
Belhajjame, Khalid
Goble, Carole A
Karagöz, Pınar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Many scientists are using workflows to systematically design and run computational experiments. Once the workflow is executed, the scientist may want to publish the dataset generated as a result, to be, e.g., reused by other scientists as input to their experiments. In doing so, the scientist needs to curate such dataset by specifying metadata information that describes it, e.g. its derivation history, origins and ownership. To assist the scientist in this task, we explore in this paper the use of provenance traces collected by workflow management systems when enacting workflows. Specifically, we identify the shortcomings of such raw provenance traces in supporting the data publishing task, and propose an approach whereby distilled, yet more informative, provenance traces that are fit for the data publishing task can be derived.

URI

https://hdl.handle.net/11511/35728

DOI

https://doi.org/10.1145/2457317.2457370

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Pınar, K. Belhajjame, C. A. Goble, and P. Karagöz, “Enhancing and abstracting scientific workflow provenance for data publishing,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35728.