TY - GEN
T1 - MLearn
T2 - 3rd Workshop on Data Management for End-To-End Machine Learning, DEEM 2019 - In conjunction with the 2019 ACM SIGMOD/PODS Conference
AU - Schüle, Maximilian E.
AU - Bungeroth, Matthias
AU - Kemper, Alfons
AU - Günnemann, Stephan
AU - Neumann, Thomas
N1 - Publisher Copyright:
© 2019 ACM.
PY - 2019/6/30
Y1 - 2019/6/30
N2 - This paper outlines the requirements of our ML2SQL compiler that allows a dedicated machine learning language (MLearn) to be run on different target architectures. The language was designed to cover an end-to-end machine learning process, including initial data curation, with the focus on moving computations inside the core of database systems. To move computations to the data, we explain the architecture of a compiler that translates into target specific user-defined-functions for the PostgreSQL and HyPer database systems. For computations inside user-defined-functions, we explain the necessary tensor datatypes and the corresponding functions. We base the explanations on an accompanying example of linear regression. To face the challenges to database systems arising from array-like data, we propose such solutions as integrating ArrayQL as stored procedures to unify the relational and array perspectives.
AB - This paper outlines the requirements of our ML2SQL compiler that allows a dedicated machine learning language (MLearn) to be run on different target architectures. The language was designed to cover an end-to-end machine learning process, including initial data curation, with the focus on moving computations inside the core of database systems. To move computations to the data, we explain the architecture of a compiler that translates into target specific user-defined-functions for the PostgreSQL and HyPer database systems. For computations inside user-defined-functions, we explain the necessary tensor datatypes and the corresponding functions. We base the explanations on an accompanying example of linear regression. To face the challenges to database systems arising from array-like data, we propose such solutions as integrating ArrayQL as stored procedures to unify the relational and array perspectives.
KW - Database scripting languages
KW - Declarative language
KW - SQL
UR - http://www.scopus.com/inward/record.url?scp=85074454661&partnerID=8YFLogxK
U2 - 10.1145/3329486.3329494
DO - 10.1145/3329486.3329494
M3 - Conference contribution
AN - SCOPUS:85074454661
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
BT - Proceedings of the 3rd Workshop on Data Management for End-To-End Machine Learning, DEEM 2019 - In conjunction with the 2019 ACM SIGMOD/PODS Conference
PB - Association for Computing Machinery
Y2 - 30 June 2019
ER -