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Scientists are now faced with an incredible volume of data to analyze. To explore and understand the data, they need to assemble complex workflows (pipelines) to manipulate the data and create insightful visual representations. Provenance is essential in this process. The provenance of a digital artifact contains information about the process and data used to derive the artifact. This information is essential to preserve the data, to determining the data's quality and authorship, aswell as for reproducing and validating results -- all important elements of the scientific process. In this survey, we aim to inform computational and visualization scientists, users and developers about different approaches to provenance management. Using the VisTrails system as a basis and real application scenarios, we will cover different approaches to acquiring and reusing provenance, including techniques that attendees can use to provenance-enable their own tools. We will also present a series operations and user interfaces that leverage provenance for tasks that go beyond reproducibility, such as aiding users in the process of pipeline design and refinement, in performing comparative analysis and visualization, supporting collaborative data exploration as well as the publication of documented, reproducible results.
Silva et al. (Sun,) studied this question.
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