SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines
Summary: SemPipes adds declarative, LLM-powered semantic operators to ML pipelines, combining natural-language data operations with arbitrary Python. SemPiper interactively visualizes synthesized implementations and evolutionary optimization conditioned on data and pipeline context. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Olga Ovcharenko (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Luciano Duarte (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 3. Sebastian Schelter (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@article{ovcharenko_vldb26,
title = {{SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines}},
author = {Ovcharenko, Olga and Duarte, Luciano and Schelter, Sebastian},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4538--4541},
doi = {10.14778/3827998.3828060},
url = {https://doi.org/10.14778/3827998.3828060},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 748 | Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing | 2025 | CIDR | 0.00014281926 |
| 2,187 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD | 8.8896655e-05 |
| 2,982 | Semantic Operators and Their Optimization: Enabling LLM-Based Data Processing with Accuracy Guarantees in LOTUS | 2025 | VLDB | 7.7845174e-05 |
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