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 (Arizona State University)
- 2. Ziyang Liu (Arizona State University)
- 3. Sivaramakrishnan Natarajan (Arizona State University)
- 4. Susan B. Davidson (University of Pennsylvania)
- 5. Yi Chen (Arizona State University)
BibTeX Citation
@article{sun_vldb09,
title = {{WOLVES: Achieving Correct Provenance Analysis by Detecting and Resolving Unsound Workflow Views}},
author = {Sun, Peng and Liu, Ziyang and Natarajan, Sivaramakrishnan and Davidson, Susan B. and Chen, Yi},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687553.1687606},
url = {https://doi.org/10.14778/1687553.1687606},
year = {2009}
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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 |
|---|---|---|---|---|
| 919 | Provenance and Scientific Workflows: Challenges and Opportunities | 2008 | SIGMOD | 0.00013214041 |
| 7,416 | Detecting and Resolving Unsound Workflow Views for Correct Provenance Analysis | 2009 | SIGMOD | 5.6237046e-05 |
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