Automatic vs Manual Provenance Abstractions: Mind the Gap
This addresses the problem of provenance abstraction for scientists needing to filter report-worthy results, but it is incremental as it compares existing approaches.
The paper compared manual workflow design abstractions with semi-automatic provenance abstraction systems (ZOOM UserViews and Workflow Summaries) in scientific workflows, finding that while process perspectives largely overlap, there is a dramatic mismatch in retained data artifacts.
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 sub-workflows. 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 and Workflow 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.