Machine Learning over Static and Dynamic Relational Data

Ahmet Kara, Milos Nikolic, Dan Olteanu, Haozhe Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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
Original languageEnglish
Title of host publicationProceedings of the 15th ACM International Conference on Distributed and Event-based Systems
Place of PublicationVirtual Event, Italy
PublisherACM
Pages160--163
Number of pages4
ISBN (Print)9781450385558
DOIs
Publication statusPublished - 28 Jun 2021
EventThe 15th ACM International Conference on Distributed and Event-based Systems - Virtual, Milan, Italy
Duration: 28 Jun 20212 Jul 2021
Conference number: 15
https://2021.debs.org/

Publication series

NameDEBS '21
PublisherACM

Conference

ConferenceThe 15th ACM International Conference on Distributed and Event-based Systems
Abbreviated titleDEBS 2021
Country/TerritoryItaly
CityMilan
Period28/06/212/07/21
Internet address

Keywords

  • machine learning models
  • incremental maintenance
  • in-database machine learning

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