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)
Incoming Non-self Citations Over Time
Authors
- 1. Raoni Lourenço (New York University)
- 2. Juliana Freire (New York University)
- 3. Dennis Shasha (New York University)
BibTeX Citation
@inproceedings{lourenco_sigmod20,
title = {{BugDoc: A System for Debugging Computational Pipelines}},
author = {Lourenço, Raoni and Freire, Juliana and Shasha, Dennis},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3384692},
url = {https://dl.acm.org/doi/10.1145/3318464.3384692},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,884 | Explaining Inference Queries with Bayesian Optimization | 2021 | VLDB | 5.7465524e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 858 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.0001356511 |
| 2,759 | Data X-Ray: A Diagnostic Tool for Data Errors | 2015 | SIGMOD | 8.1577506e-05 |
| 2,807 | Data Provenance at Internet Scale: Architecture, Experiences, and the Road Ahead | 2017 | CIDR | 8.1009158e-05 |
| 8,185 | BugDoc: Algorithms to Debug Computational Processes | 2020 | SIGMOD | 5.4707797e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,353 | Reconstructing and Querying ML Pipeline Intermediates | 2023 | CIDR |
| 2 | 10,018 | From Logs to Causal Inference: Diagnosing Large Systems | 2025 | VLDB |
| 3 | 11,047 | APEX-DAG: Library and Language independent Pipeline EXtraction | 2025 | VLDB |
| 4 | 8,171 | Fixed It For You: Protocol Repair Using Lineage Graphs | 2019 | CIDR |
| 5 | 4,518 | MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines | 2021 | SIGMOD |
| 6 | 9,245 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB |
| 7 | 8,482 | A Demonstration of DLBD: Database Logic Bug Detection System | 2023 | VLDB |
| 8 | 4,756 | Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture | 2020 | VLDB |
| 9 | 9,495 | Debugging Large-Scale Data Science Pipelines using Dagger | 2020 | VLDB |
| 10 | 8,185 | BugDoc: Algorithms to Debug Computational Processes | 2020 | SIGMOD |