Abstract
The present master’s thesis examines the implementation and evaluation of data-sketchingalgorithms with a specific focus on the application of data sketches for cardinality estimation, which is a crucial factor in the effectiveness and efficiency of query optimization
in modern database systems. Given a range of existing implementations of state-of-theart data-sketching algorithms, this work aims to implement and investigate approaches
for data-sketching algorithms described in recent scientific literature. These newly implemented algorithms will undergo quantitative and qualitative evaluations, with their
performance determined based on various metrics and put into context with the properties derived from the qualitative analysis. The significance of this work lies in improving
the accuracy and efficiency of query optimization through the use of these investigated
data-sketching methods. Insights into the practical implementation of data-sketching
algorithms are to be gained. Furthermore, through the implementation of the selected
data sketching algorithms, a practical foundation for more efficient estimations of the
cardinality of join operations in database systems may be produced, ultimately providing comprehensive evaluation data as a reference for future work in this field.
| Date of Award | 2024 |
|---|---|
| Original language | English (American) |
| Supervisor | Gabriel Kronberger (Supervisor) |