Optimizing SQL query execution through effective cost models is a critical challenge in database management systems (DBMS). This thesis introduces a modular benchmarking system for cost models, with a pluggable architecture for both cost models and execution engines, enabling comprehensive benchmarking across various scenarios. Accompanied by a detailed methodology for the empirical measurement of cost model performance across different execution engines, a standardized approach is established, ensuring consistent and reproducible benchmarks. Furthermore, as a showcase of the developed system's capabilities, an analysis of key features influencing join-order optimization performance in both CPU and GPU systems is presented. This analysis demonstrates the system's utility in developing more effective cost models and optimizers. These contributions pave the way for future research in DBMS optimization, providing a research platform for the accelerated development of new cost models.

This research builds upon a previous method of vectorisation of pixel art by pixel neighbour connecting and boundary energy minimisation. The downside of the previous method is the lack of user input throughout the process and possible divergence between the results and the artist’s vision. The proposed method uses the first part of previous method to connect neighbour pixels and continues with a spring simulation of the boundaries. A process that can be heavily user-guided with adjusting the stiffness of the springs. Results show the proposed method to achieve higher variance in the results, possibly resulting in output images closer to the artist’s vision. However, introduce lengthy process for the user that can be removed by providing higher level GUI.