PipeLens: Identifying Interventions for Resolving Malfunctioning Data Science Pipelines
Summary: PipeLens diagnoses malfunctioning data-science pipelines via causal interventions on DAG-structured modules and parameters, learning a utility proxy from successful/failed runs. It identifies causally verified root causes and efficient repairs, outperforming baselines across real datasets. (summarized by gpt-5.6-luna on Aug 28 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Jahid Hasan (Purdue University)
- 2. Stanley Jiang (Cornell University)
- 3. Tejendra Singh (Purdue University)
- 4. Sainyam Galhotra (Cornell University)
- 5. Romila Pradhan (Purdue University)
- 6. Divesh Srivastava (AT&T)
BibTeX Citation
@article{hasan_vldb26,
title = {{PipeLens: Identifying Interventions for Resolving Malfunctioning Data Science Pipelines}},
author = {Hasan, Jahid and Jiang, Stanley and Singh, Tejendra and Galhotra, Sainyam and Pradhan, Romila and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3188--3201},
doi = {10.14778/3836663.3836682},
url = {https://doi.org/10.14778/3836663.3836682},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,924 | CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning | 2024 | SIGMOD |
| 2 | 11,413 | mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines” | 2025 | VLDB |
| 3 | 7,544 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD |
| 4 | 5,574 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD |
| 5 | 4,608 | MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines | 2021 | SIGMOD |
| 6 | 9,682 | Debugging Large-Scale Data Science Pipelines using Dagger | 2020 | VLDB |
| 7 | 6,327 | Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines | 2021 | CIDR |
| 8 | 9,426 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB |
| 9 | 11,677 | Reconstructing and Querying ML Pipeline Intermediates | 2023 | CIDR |
| 10 | 13,754 | Data Cleaning in the Era of Data Science: Challenges and Opportunities | 2021 | CIDR |