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
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 language | English |
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Title of host publication | Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems |
Place of Publication | Virtual Event, Italy |
Publisher | ACM |
Pages | 160--163 |
Number of pages | 4 |
ISBN (Print) | 9781450385558 |
DOIs | |
Publication status | Published - 28 Jun 2021 |
Event | The 15th ACM International Conference on Distributed and Event-based Systems - Virtual, Milan, Italy Duration: 28 Jun 2021 → 2 Jul 2021 Conference number: 15 https://2021.debs.org/ |
Publication series
Name | DEBS '21 |
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Publisher | ACM |
Conference
Conference | The 15th ACM International Conference on Distributed and Event-based Systems |
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Abbreviated title | DEBS 2021 |
Country/Territory | Italy |
City | Milan |
Period | 28/06/21 → 2/07/21 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- machine learning models
- incremental maintenance
- in-database machine learning