Automatic vs Manual Provenance Abstractions: Mind the Gap

Pinar Alper, Khalid Belhajjame, Carole Goble

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    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.
    Original languageEnglish
    Title of host publicationProceedings of the 2016 workshop on Theory and Practice of Provenance
    Publication statusPublished - 8 Jun 2016

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