Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning
Summary: OpenDBML provides a unifying Python API and web-based extension points for diverse in-database ML engines and a curated dataset library, abstracting away ad-hoc UIs and proprietary data formats. Demo scenarios showcase plug-in of new systems/datasets and end-to-end, join-free in-DB ML workflows. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Mahdi Ghorbani
- 2. Amir Shaikhha
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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 |
|---|---|---|---|---|
| 851 | Scaling Factorization Machines to Relational Data | 2013 | VLDB | 0.00015909639 |
| 1,172 | Learning Generalized Linear Models Over Normalized Data | 2015 | SIGMOD | 0.00013504249 |
| 1,283 | Towards Linear Algebra over Normalized Data | 2017 | VLDB | 0.00012826013 |
| 2,197 | Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra | 2019 | SIGMOD | 9.3117431e-05 |
| 3,280 | A Layered Aggregate Engine for Analytics Workloads | 2019 | SIGMOD | 7.2813732e-05 |
| 4,787 | The Relational Data Borg is Learning | 2020 | VLDB | 5.9168117e-05 |
| 8,753 | An Intermediate Representation for Hybrid Database and Machine Learning Workloads | 2021 | VLDB | 4.4520434e-05 |
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