An outline of multi objective optimization in databases with focus on flexible skyline queries
Matteo Savino

TL;DR
This paper reviews multi-objective optimization techniques in databases, emphasizing the flexible skyline paradigm as an effective approach to handle conflicting criteria in large data sets.
Contribution
It provides a comprehensive overview of multi-criteria optimization methods in databases, highlighting the advantages of the flexible skyline approach over traditional methods.
Findings
Flexible skyline overcomes critical issues of other methods
Thorough comparison of ranking, skyline, and advanced techniques
Clarifies the role of multi-objective optimization in large-scale database queries
Abstract
The problem of optimizing across different, conceivably conflicting, criteria is called multi-objective optimization and it is widely spread across many fields. This is a recurring problem in database queries when there is the need of obtaining the best objects from a very large data set. In this article, I included a complete review of the main approaches typically used to achieve multi-criteria optimization. Starting from ranking queries and skylines and then proceeding to more advanced methods, this paper aims to define a clear outline of multi-objective optimization in databases. In particular, the flexible skyline paradigm is considered and thoroughly discussed as it overcomes many of the critical issues that arise with other methods.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Constraint Satisfaction and Optimization · Geographic Information Systems Studies
