In-database machine learning: Gradient descent and tensor algebra for main memory database systems

Maximilian Schüle, Frédéric Simonis, Thomas Heyenbrock, Alfons Kemper, Stephan Günnemann, Thomas Neumann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

8 Zitate (Scopus)

Abstract

Machine learning tasks such as regression, clustering, and classification are typically performed outside of database systems using dedicated tools, necessitating the extraction, transformation, and loading of data. We argue that database systems when extended to enable automatic differentiation, gradient descent, and tensor algebra are capable of solving machine learning tasks more efficiently by eliminating the need for costly data communication. We demonstrate our claim by implementing tensor algebra and stochastic gradient descent using lambda expressions for loss functions as a pipelined operator in a main memory database system. Our approach enables common machine learning tasks to be performed faster than by extended disk-based database systems or as well as dedicated tools by eliminating the time needed for data extraction. This work aims to incorporate gradient descent and tensor data types into database systems, allowing them to handle a wider range of computational tasks.

OriginalspracheEnglisch
TitelDatenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019
Redakteure/-innenTorsten Grust, Felix Naumann, Alexander Bohm, Wolfgang Lehner, Theo Harder, Erhard Rahm, Andreas Heuer, Meike Klettke, Holger Meyer
Herausgeber (Verlag)Gesellschaft fur Informatik (GI)
Seiten247-266
Seitenumfang20
ISBN (elektronisch)9783885796831
DOIs
PublikationsstatusVeröffentlicht - 2019
VeranstaltungDatenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019 - Database Systems for Business, Technology and Web, BTW 2019 and 18th Symposium of the GI Department "Databases and Information Systems", DBIS 2019 - Rostock, Deutschland
Dauer: 4 März 20198 März 2019

Publikationsreihe

NameLecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
BandP-289
ISSN (Print)1617-5468
ISSN (elektronisch)2944-7682

Konferenz

KonferenzDatenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019 - Database Systems for Business, Technology and Web, BTW 2019 and 18th Symposium of the GI Department "Databases and Information Systems", DBIS 2019
Land/GebietDeutschland
OrtRostock
Zeitraum4/03/198/03/19

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