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Home » Automatic Versus Manual Provenance Abstractions: Mind the Gap
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Automatic Versus Manual Provenance Abstractions: Mind the Gap

Authors: 

Pinar Alper, University of Manchester; Khalid Belhajjame, Université Paris Dauphine; Carole A. Goble, University of Manchester

Abstract: 

In recent years the need to simplify or to hide sensitive information in provenance has given way to research on provenance abstraction. In the context of scientific workflows, existing research provides techniques to semi-automatically create abstractions of a given workflow description, which is in turn used as filters over the workflow’s provenance traces. An alternative approach that is commonly adopted by scientists is to build workflows with abstractions embedded into the workflow’s design, such as using subworkflows. This paper reports on the comparison of manual versus semi-automated approaches in a context where result abstractions are used to filter report-worthy results of computational scientific analyses. Specifically; we take a real-world workflow containing user-created design abstractions and compare these with abstractions created by ZOOM*UserViews andWorkflow Summaries systems. Our comparison shows that semi-automatic and manual approaches largely overlap from a process perspective, meanwhile, there is a dramatic mismatch in terms of data artefacts retained in an abstracted account of derivation.We discuss reasons and suggest future research directions.

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BibTeX
@inproceedings {196990,
title = {Automatic Versus Manual Provenance Abstractions: Mind the Gap},
booktitle = {8th USENIX Workshop on the Theory and Practice of Provenance (TaPP 16)},
year = {2016},
address = {Washington, D.C.},
url = {https://www.usenix.org/conference/tapp16/workshop-program/presentation/alper},
publisher = {USENIX Association},
month = jun,
}
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