Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines
Summary: mlwhatif declaratively specifies data-centric what-if analyses over ML pipelines and auto-generates variants via patches. A 4-rule optimizer executes variants; instrumented dataflow plans enable linear speedups (up to 13x) and data-size independence. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Stefan Grafberger (University of Amsterdam)
- 2. Paul Groth (University of Amsterdam)
- 3. Sebastian Schelter (University of Amsterdam)
BibTeX Citation
@inproceedings{grafberger_sigmod23,
title = {{Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines}},
author = {Grafberger, Stefan and Groth, Paul and Schelter, Sebastian},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589273},
url = {https://dl.acm.org/doi/10.1145/3589273},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,742 | Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine | 2025 | SIGMOD | 5.6910432e-05 |
| 7,629 | mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? | 2023 | VLDB | 5.4803665e-05 |
| 9,117 | Modyn: Data-Centric Machine Learning Pipeline Orchestration | 2025 | SIGMOD | 5.2263399e-05 |
| 10,043 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 5.0921006e-05 |
| 10,515 | Understanding the Impact of Data Noise in Federated Learning: [Experiments & Analysis] | 2026 | SIGMOD | 4.9793485e-05 |
| 10,898 | stratum: A System Infrastructure for Massive Agent-Centric ML Workloads | 2026 | VLDB | 4.9793485e-05 |
| 10,945 | Morphing-based Compression for Data-centric ML Pipelines | 2026 | VLDB | 4.9793485e-05 |
| 11,407 | mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines” | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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