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Workflow views abstract groups of tasks in a workflow into composite tasks, and are used for simplifying provenance analysis, workflow sharing and reuse. An unsound view does not preserve the dataflow between tasks in the workflow, and can therefore cause incorrect provenance analysis. In this demo the authors present WOLVES, a system that efficiently identifies and corrects unsound workflow views with minimal changes (view correction). Since the view correction problem is NP-hard, WOLVES allows the user to choose between two forms of local optimality, strong and weak. Efficient time algorithms achieving these optimalities are implemented in WOLVES.
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