Query Optimization using Graph Neural Networks Databases and Information Systems


Query Optimization is a critical component in achieving optimal runtime for database queries. One important factor in query optimization is determining the expected cardinality of subqueries. To achieve this, cardinality estimation algorithms are commonly used in query engines.

The aim of this thesis is to integrate a recent approach that estimates the cardinality of conjunctive queries over Knowledge Graphs using Graph Neural Networks and Knowledge Graph Embeddings. This approach has demonstrated high accuracy in estimating query cardinalities. The central question to be investigated is whether and to what extent this new approach can enhance query optimization.

The student will be tasked with integrating the new approach into various query engines and benchmarking the results against different combinations of engines and cardinality estimators. This will provide valuable insights into the effectiveness of the new approach in improving query optimization.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210