Description

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.