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NN2SQL : Let SQL Think for Neural Networks
Schüle, Maximilian; Kemper, Alfons; Neumann, Thomas (2023): NN2SQL : Let SQL Think for Neural Networks, in: Bamberg: Otto-Friedrich-Universität, S. 183–194.
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Publisher Information:
Year of publication:
2023
Pages:
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
Year of first publication:
2023
Language:
English
Abstract:
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
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Type:
Conferenceobject
Activation date:
May 26, 2023
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https://fis.uni-bamberg.de/handle/uniba/59172