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BugDoc: A System for Debugging Computational Pipelines

Summary: Provenance-driven automatic root-cause inference for complex pipelines, with iterative, succinct failure explanations. BugDoc demonstrates debugging from few configurations, enabling automatic triage and actionable insights for data-intensive workflows. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5866
Venue
SIGMOD
Year
2020
Pagerank
4.3660299e-05
Overall Rank
9,223 | 35.90%
DOI
10.1145/3318464.3384692

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
6,779 Explaining Inference Queries with Bayesian Optimization 2021 VLDB 4.9232829e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,096 Interpretable and Informative Explanations of Outcomes 2015 VLDB 0.00014088686
2,903 Data Provenance at Internet Scale: Architecture, Experiences, and the Road Ahead 2017 CIDR 7.9403877e-05
3,107 Data X-Ray: A Diagnostic Tool for Data Errors 2015 SIGMOD 7.5549177e-05
8,335 BugDoc: Algorithms to Debug Computational Processes 2020 SIGMOD 4.538972e-05
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