Transforming ML Predictive Pipelines into SQL with MASQ
Summary: MASQ compiles trained ML pipelines (scikit-learn) into standard SQL for on-DBMS inference, with no UDFs or vendor-specific syntax. Eliminating data movement, it leverages DBMS governance, security, and auditability for portable deployment across DBMSs (MySQL, SQL Server) and GUI-based evaluation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Francesco Del Buono (University of Modena and Reggio Emilia)
- 2. Matteo Paganelli (University of Modena and Reggio Emilia)
- 3. Paolo Sottovia (Huawei)
- 4. Matteo Interlandi (Microsoft)
- 5. Francesco Guerra (University of Modena and Reggio Emilia)
BibTeX Citation
@inproceedings{buono_sigmod21,
title = {{Transforming ML Predictive Pipelines into SQL with MASQ}},
author = {Del Buono, Francesco and Paganelli, Matteo and Sottovia, Paolo and Interlandi, Matteo and Guerra, Francesco},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452771},
url = {https://dl.acm.org/doi/10.1145/3448016.3452771},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,823 | Query Processing on Tensor Computation Runtimes | 2022 | VLDB | 8.0893814e-05 |
| 5,953 | The Tensor Data Platform: Towards an AI-centric Database System | 2023 | CIDR | 6.0309873e-05 |
| 8,716 | nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems | 2024 | VLDB | 5.3776746e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 106 | The MADlib Analytics Library or MAD Skills, the SQL | 2012 | VLDB | 0.00033539462 |
| 518 | Towards a Unified Architecture for in-RDBMS Analytics | 2012 | SIGMOD | 0.00017167492 |
| 2,293 | Extending Relational Query Processing with ML Inference | 2020 | CIDR | 8.7949378e-05 |
| 3,614 | Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML | 2020 | CIDR | 7.2568185e-05 |
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