# Machine Learning over Static and Dynamic Relational Data

arXiv: https://arxiv.org/abs/2107.13923 · HTML: https://mlsanity.com/paper/2107.13923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases](https://mlsanity.com/paper/2507.12562/) (2025)

## Abstract (arXiv)

This tutorial overviews principles behind recent works on training and maintaining machine learning models over relational data, with an emphasis on the exploitation of the relational data structure to improve the runtime performance of the learning task. The tutorial has the following parts: 1) Database research for data science 2) Three main ideas to achieve performance improvements 2.1) Turn the ML problem into a DB problem 2.2) Exploit structure of the data and problem 2.3) Exploit engineering tools of a DB researcher 3) Avenues for future research