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)

Paper ID: h377fc1a40e43663e
Venue: VLDB
Year: 2025
Pagerank: 4.9793485e-05
Overall Rank: 11,407 | 23.31%
DOI: 10.14778/3750601.3750671

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

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)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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
442	Differential dataflow	2013	CIDR	0.00018210463
483	ActiveClean: Interactive Data Cleaning For Statistical Modeling	2016	VLDB	0.00017590977
894	Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms	2019	VLDB	0.00013212578
1,162	Responsible Data Management	2020	VLDB	0.00011753159
1,941	Interpretable Data-Based Explanations for Fairness Debugging	2022	SIGMOD	9.3297671e-05
2,326	SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging	2021	SIGMOD	8.6309237e-05
4,940	Explaining Outputs in Modern Data Analytics	2016	VLDB	6.345789e-05
7,538	Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines	2023	SIGMOD	5.4995874e-05

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