Rank4QO: Revolutionizing Query Optimization with Ranking Algorithms

Name of applicant

Zoi Kaoudi


Associate Professor


IT University of Denmark


DKK 4,999,884



Type of grant

Semper Ardens: Accelerate


Query optimization in data systems is crucial for efficient query execution. AI/ML have been recently proven very effective tools in enhancing this process, reducing efficiency and manual effort. This project aims to explore the potential of ranking and develop specialized ranking methods to improve query performance. Our vision is for the project to pioneer a new research paradigm.


Query optimization emerges as a critical source of optimal data-to-insights time, cost savings and energy efficiency. Despite the potential of ML-based approaches based on regression models, they have yet to achieve optimal performance. In this project, we will investigate what really matters for efficient query optimization, i.e., the relative order of query plans and, thus, ranking algorithms.


The aim of Rank4QO is to explore ranking methods and devise specialized algorithms to enhance query performance. To achieve this, we will conduct a thorough analysis of the connections between queries and query plans, develop learning-to-rank models, and new optimization algorithms. Ultimately, Rank4QO will reshape the entire query optimization process by incorporating the ranking concept.

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