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“GraphQueryML: Using Machine Learning to Optimize Queries in Graph Databases” accepted by SNSF/DFG

The proposal “GraphQueryML: Using Machine Learning to Optimize Queries in Graph Databases" by Kurt Stockinger (ZHAW) and Michael Grossniklaus (University of Konstanz) was accepted by SNSF/DFG and thus will receive funding from the Swiss National Science Foundation and the German Science Foundation (Deutsche Forschungsgemeinschaft). Ce Zhang (ETH Zurich) is the advisor of the project.

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.