Investigation of the Efficiency of Window Functions in Query Optimization and Big Data Processing in Relational Databases
Students Name: Horskyi Bohdan Mykhailovych
Qualification Level: magister
Speciality: Applied Mathematics
Institute: Institute of Applied Mathematics and Fundamental Sciences
Mode of Study: full
Academic Year: 2024-2025 н.р.
Language of Defence: ukrainian
Abstract: In this thesis, research was conducted on query optimization in relational databases and the efficiency of window functions in processing large datasets. A series of queries were developed using window functions and traditional methods, and multiple experiments were conducted with varying data volumes. Based on the results, graphs were created to illustrate the findings. The analytical properties of window functions were compared with those of traditional functions in data aggregation. Query optimization was performed using INDEX and CLUSTERED INDEX, and the impact of both maximum and minimum levels of parallelism (the MAXDOP setting) on performance and execution time was analyzed.