mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines”
Summary: mlidea interactively improves ML data-preparation code through hidden “shadow pipelines” that detect issues, test revisions, and explain suggestions. Incremental view maintenance keeps these variants low-latency, enabling interactive fixes for labels, data quality, and slice-specific performance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Stefan Grafberger (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Paul Groth (University of Amsterdam)
- 3. Sebastian Schelter (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@article{grafberger_vldb25,
title = {{mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines”}},
author = {Grafberger, Stefan and Groth, Paul and Schelter, Sebastian},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5359--5362},
doi = {10.14778/3750601.3750671},
url = {https://doi.org/10.14778/3750601.3750671},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 455 | Differential dataflow | 2013 | CIDR | 0.00018133241 |
| 582 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016148948 |
| 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012333161 |
| 1,340 | Responsible Data Management | 2020 | VLDB | 0.00011111667 |
| 1,951 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 9.4252389e-05 |
| 2,273 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 8.8230899e-05 |
| 4,845 | Explaining Outputs in Modern Data Analytics | 2016 | VLDB | 6.4818607e-05 |
| 7,395 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD | 5.6257796e-05 |
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