Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines
Summary: mlinspect: a library that extracts a DAG representation of Python ML preprocessing pipelines to enable lightweight lineage-based inspection of brittle data issues affecting reliability, accountability, and fairness. Automatically instruments declarative abstractions (estimator/transformer pipelines) via lightweight annotation propagation—no manual code instrumentation—enabling end-to-end inspection in native ML stacks. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Stefan Grafberger (Technical University of Munich)
- 2. Julia Stoyanovich (New York University)
- 3. Sebastian Schelter (University of Amsterdam)
BibTeX Citation
@inproceedings{grafberger_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Grafberger, Stefan and Stoyanovich, Julia and Schelter, Sebastian},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,662 | End-to-end Optimization of Machine Learning Prediction Queries | 2022 | SIGMOD | 8.1596229e-05 |
| 4,334 | LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems | 2021 | SIGMOD | 6.6569314e-05 |
| 4,606 | MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines | 2021 | SIGMOD | 6.5041225e-05 |
| 9,004 | The Cost of Representation by Subset Repairs | 2025 | VLDB | 5.2389669e-05 |
| 11,628 | LucidScript: Bottom-up Standardization for Data Preparation | 2024 | VLDB | 4.9793485e-05 |
| 11,818 | Screening Native ML Pipelines with “ArgusEyes” | 2022 | CIDR | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 17 | Provenance Semirings | 2007 | PODS | 0.00059752575 |
| 1,162 | Responsible Data Management | 2020 | VLDB | 0.00011753159 |
| 1,193 | Weld: A Common Runtime for High Performance Data Analytics | 2017 | CIDR | 0.0001158809 |
| 1,308 | Automating Large-Scale Data Quality Verification | 2018 | VLDB | 0.0001107886 |
| 1,691 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 9.8570722e-05 |
| 1,765 | Putting Lipstick on Pig: Enabling Database-style Workflow Provenance | 2012 | VLDB | 9.6955585e-05 |
| 1,825 | Data Management for Data Science: Towards Embedded Analytics | 2020 | CIDR | 9.5603293e-05 |
| 2,445 | noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts | 2017 | VLDB | 8.4570447e-05 |
| 4,449 | Data Debugging and Exploration with Vizier | 2019 | SIGMOD | 6.5953404e-05 |
| 5,252 | Dagger: A Data (not code) Debugger | 2020 | CIDR | 6.2087063e-05 |
| 5,574 | Inspector Gadget: A Framework for Custom Monitoring and Debugging of Distributed Dataflows | 2011 | SIGMOD | 6.0794039e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,407 | mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines” | 2025 | VLDB |
| 2 | 11,818 | Screening Native ML Pipelines with “ArgusEyes” | 2022 | CIDR |
| 3 | 7,629 | mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? | 2023 | VLDB |
| 4 | 11,410 | APEX-DAG: Library and Language independent Pipeline EXtraction | 2025 | VLDB |
| 5 | 7,212 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB |
| 6 | 11,671 | Reconstructing and Querying ML Pipeline Intermediates | 2023 | CIDR |
| 7 | 9,417 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB |
| 8 | 7,538 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD |
| 9 | 11,821 | Towards Observability for Machine Learning Pipelines | 2022 | CIDR |
| 10 | 4,606 | MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines | 2021 | SIGMOD |