Maschinelles Lernen für Kardinalitätsschätzungen in relationalen Datenbanken

Query optimization in relational databases is heavily based on cardinality estimation, i.e., estimating the result size of an input query. With the recent developments in Machine Learning, researchers and practitioners see in Learned Cardinality Estimation an alternative to produce accurate estimations. Hence, Learned Cardinality Estimation can be formulated as a learning task (e.g., regression), where query features are used as input and the estimated cardinality as output. This thesis aims to investigate, evaluate, and compare current Machine Learning approaches for Cardinality Estimation under distinct settings (e.g., dataset imbalance, different feature extraction techniques, diverse learning algorithms).