English

F-IVM: Learning over Fast-Evolving Relational Data

Databases 2020-06-02 v1

Abstract

F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstrate F-IVM for three such applications: model selection, Chow-Liu trees, and ridge linear regression.

Keywords

Cite

@article{arxiv.2006.00694,
  title  = {F-IVM: Learning over Fast-Evolving Relational Data},
  author = {Milos Nikolic and Haozhe Zhang and Ahmet Kara and Dan Olteanu},
  journal= {arXiv preprint arXiv:2006.00694},
  year   = {2020}
}

Comments

SIGMOD DEMO 2020, 5 pages

R2 v1 2026-06-23T15:57:02.809Z