Managing Provenance through User Views in Scientific Workflows
Sarah Cohen Boulakia, Olivier Biton, Susan Davidson, and Shirley Cohen

Abstract
Scientific workflow systems have become increasingly popular for
managing large-scale in-silico experiments where many bioinformatics
tasks are chained together. Due to the large amount of data products
generated by these experiments and the need for reproducible results,
provenance has become of paramount importance. Although many workflow
systems keep track of data provenance, they do not provide support for
querying provenance information. One contribution of this work is a
generic and minimal model for data provenance.

However, there is also an interesting interaction between provenance and
``user views." Since bioinformatics tasks may themselves be complex
sub-workflows, the notion of a user view determines what level of
granularity the user can see in the workflow. For example, biologists
may simply wish a view in which reformatting tasks are hidden and
biologically relevant tasks are seen. Thus the user view determines what
data products and tasks can be seen and queried when answering questions
of provenance. The main contribution of this work is therefore to
formalize the notion of user views, demonstrate how they can be used in
provenance queries, and give an algorithm for generating biologically
relevant user views. We also present a prototype based on these ideas
and give performance results.