NN2SQL : Let SQL Think for Neural Networks
Faculty/Professorship: | Data Engineering |
Author(s): | Schüle, Maximilian ![]() |
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 |
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 |
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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originated at the
University of Bamberg
University of Bamberg