Incorporating Deep Learning Design in Database Queries
Deep learning over relational databases is conventionally realized by translating data into graph representations and applying graph-based neural networks within external frameworks. This round-trip between the database and external machine learning (ML) systems introduces non-trivial engineering overhead. In effect, these graph neural networks operate on tuple embeddings and manipulate them in ways that capture the interactions induced by relational joins. Given this natural correspondence, there is no fundamental reason why specifying a neural network over relational data should be substantially harder than querying it. We propose an approach that naturally integrates deep learning with...
| Authors | Yuval Lev Lubarsky, Dean Light, Boaz Berger et al. |
|---|---|
| Primary category | cs.DB |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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