TY - GEN
T1 - NN2SQL
T2 - 2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 Database Systems for Business, Technology and Web, BTW 2023
AU - Schüle, Maximilian E.
AU - Kemper, Alfons
AU - Neumann, Thomas
N1 - Publisher Copyright:
© 2023 Gesellschaft fur Informatik (GI). All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Automatic Differentiation
KW - Neural Networks
KW - SQL-92
UR - http://www.scopus.com/inward/record.url?scp=85149930037&partnerID=8YFLogxK
U2 - 10.18420/BTW2023-09
DO - 10.18420/BTW2023-09
M3 - Conference contribution
AN - SCOPUS:85149930037
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
SP - 183
EP - 194
BT - Datenbanksysteme fur Business, Technologie und Web, BTW 2023
A2 - Konig-Ries, Birgitta
A2 - Scherzinger, Stefanie
A2 - Lehner, Wolfgang
A2 - Vossen, Gottfried
PB - Gesellschaft fur Informatik (GI)
Y2 - 6 March 2023 through 10 March 2023
ER -