WOLVES: Achieving Correct Provenance Analysis by Detecting and Resolving Unsound Workflow Views
Summary: WOLVES detects unsound workflow views that break dataflow provenance and fixes them with minimal, targeted view edits. Because view correction is NP-hard, it offers efficient time algorithms with strong or weak local optimality for scalable provenance repair. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Peng Sun
- 2. Ziyang Liu
- 3. Sivaramakrishnan Natarajan
- 4. Susan B. Davidson
- 5. Yi Chen
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 921 | Provenance and Scientific Workflows: Challenges and Opportunities | 2008 | SIGMOD | 0.00015261463 |
| 7,368 | Detecting and Resolving Unsound Workflow Views for Correct Provenance Analysis | 2009 | SIGMOD | 4.7455168e-05 |
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