GraphQueryML – Using Machine Learning to Optimize Queries in Graph Databases (SNSF/DFG)
Description
Optimizing the brain of databases with machine learning:Query optimization is one of the hardest problems of database systems research. A query optimizer can be considered as the “brain” of the system that makes sure that queries are executed efficiently. Even after several decades of research, many sub-problems of query optimization are still unsolved.
The goal of this project is to use machine learning to improve the “brain” of relational database systems as well as graph database systems.
Key data
Projectlead
Deputy Projectlead
Prof. Dr. Michael Grossniklaus
Project team
Dr. Pavel Sulimov, Claude Lehmann, Dennis Gehrig, Prof. Dr. Ce Zhang
Project partners
Universität Konstanz; Eidgenössische Technische Hochschule Zürich ETHZ
Project status
completed, 07/2021 - 12/2024
Institute/Centre
Institute of Computer Science (InIT)
Funding partner
SNF-Projektförderung ; Deutsche Forschungsgemeinschaft DFG
Project budget
539'000 EUR
Publications
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GenJoin : conditional generative plan-to-plan query optimizer that learns from subplan hints
2025 Sulimov, Pavel; Lehmann, Claude; Stockinger, Kurt
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Is your learned query optimizer behaving as you expect? : a machine learning perspective
2024 Lehmann, Claude; Sulimov, Pavel; Stockinger, Kurt
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QardEst : using quantum machine learning for cardinality estimation of join queries
2024 Kittelmann, Florian; Sulimov, Pavel; Stockinger, Kurt
School of Engineering
Obere Kirchgasse 2 / Steinberggasse 12/14
8400 Winterthur