TU Darmstadt / ULB / TUbiblio

DB4ML - An In-Memory Database Kernel with Machine Learning Support

Jasny, Matthias and Ziegler, Tobias and Kraska, Tim and Roehm, Uwe and Binnig, Carsten (2020):
DB4ML - An In-Memory Database Kernel with Machine Learning Support.
In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 159-173,
ACM, SIGMOD/PODS '20: International Conference on Management of Data, virtual Conference, 14.-19.06, ISBN 9781450367356,
DOI: 10.1145/3318464.3380575,
[Conference or Workshop Item]

Abstract

In this paper, we revisit the question of how ML algorithms can be best integrated into existing DBMSs to not only avoid expensive data copies to external ML tools but also to comply with regulatory reasons. The key observation is that database transactions already provide an execution model that allows DBMSs to efficiently mimic the execution model of modern parallel ML algorithms. As a main contribution, this paper presents DB4ML, an in-memory database kernel that allows applications to implement user-defined ML algorithms and efficiently run them inside a DBMS. Thereby, the ML algorithms are implemented using a programming model based on the idea of so called iterative transactions. Our experimental evaluation shows that DB4ML can support user-defined ML algorithms inside a DBMS with the efficiency of modern specialized ML engines. In contrast to DB4ML, these engines not only need to transfer data out of the DBMS but also hardcode the ML algorithms and thus are not extensible.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Jasny, Matthias and Ziegler, Tobias and Kraska, Tim and Roehm, Uwe and Binnig, Carsten
Title: DB4ML - An In-Memory Database Kernel with Machine Learning Support
Language: German
Abstract:

In this paper, we revisit the question of how ML algorithms can be best integrated into existing DBMSs to not only avoid expensive data copies to external ML tools but also to comply with regulatory reasons. The key observation is that database transactions already provide an execution model that allows DBMSs to efficiently mimic the execution model of modern parallel ML algorithms. As a main contribution, this paper presents DB4ML, an in-memory database kernel that allows applications to implement user-defined ML algorithms and efficiently run them inside a DBMS. Thereby, the ML algorithms are implemented using a programming model based on the idea of so called iterative transactions. Our experimental evaluation shows that DB4ML can support user-defined ML algorithms inside a DBMS with the efficiency of modern specialized ML engines. In contrast to DB4ML, these engines not only need to transfer data out of the DBMS but also hardcode the ML algorithms and thus are not extensible.

Title of Book: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Publisher: ACM
ISBN: 9781450367356
Uncontrolled Keywords: dm, dm_funding_52300543
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Data Management
Event Title: SIGMOD/PODS '20: International Conference on Management of Data
Event Location: virtual Conference
Event Dates: 14.-19.06
Date Deposited: 14 Dec 2020 09:26
DOI: 10.1145/3318464.3380575
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details