NN2SQL : Let SQL Think for Neural Networks

Faculty/Professorship: Data Engineering 
Author(s): Schüle, Maximilian  ; Kemper, Alfons; Neumann, Thomas
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2023
Pages: 183-194
Source/Other editions: Lecture Notes in Informatics (LNI) : Proceedings ; P-331 / Birgitta König-Ries, Stefanie Scherzinger, Wolfgang Lehner, Gottfried Vossen (Hg.). Bonn: Gesellschaft für Informatik e.V. , 2023, S. 183-194 - ISBN: 978-3-88579-725-8
is version of: 10.18420/BTW2023-09
Year of first publication: 2023
Language(s): English
Licence: Creative Commons - CC BY-SA - Attribution - ShareAlike 4.0 International 
URN: urn:nbn:de:bvb:473-irb-591725
Although database systems perform well in data access and manipulation, their relational model hinders data scientists from formulating machine learning algorithms in SQL. Nevertheless, we argue that modern database systems perform well for machine learning algorithms expressed in relational algebra. To overcome the barrier of the relational model, this paper shows how to transform data into a relational representation for training neural networks in SQL: We first describe building blocks for data transformation in SQL. Then, we compare an implementation for model training using array data types to the one using a relational representation in SQL-92 only. The evaluation proves the suitability of modern database systems for matrix algebra, although specialised array data types perform better than matrices in relational representation.
GND Keywords: SQL/92; Neuronales Netz; Automatische Differentiation
Keywords: SQL-92, Neural Networks, Automatic Differentiation
DDC Classification: 004 Computer science  
RVK Classification: ST 270   
Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/59172
Release Date: 26. May 2023

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