WOLVES: achieving correct provenance analysis by detecting and resolving unsound workflow views

Authors:
Peng Sun;Ziyang Liu;Sivaramakrishnan Natarajan;Susan B. Davidson;Yi Chen
Affiliations:
Arizona State University;Arizona State University;Arizona State University;University of Pennsylvania;Arizona State University
Venue:
Proceedings of the VLDB Endowment
Year:
2009

Citing 4
Cited 1

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Provenance and scientific workflows: challenges and opportunities

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Querying and Managing Provenance through User Views in Scientific Workflows

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Detecting and resolving unsound workflow views for correct provenance analysis

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data

Generating sound workflow views for correct provenance analysis

ACM Transactions on Database Systems (TODS)

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Abstract

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 we 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.