The article introduces the branch-and-bound skyline (BBS) algorithm, a novel approach to skyline computation in database systems. Skyline computation is crucial for multicriteria decision-making, and existing algorithms often have limitations in terms of performance and applicability. BBS is designed to be I/O optimal, meaning it only accesses nodes that may contain skyline points, and it supports progressive processing, allowing for efficient initial results without reading the entire dataset. The algorithm is based on nearest-neighbor search and is simple to implement, supporting various types of progressive processing. The authors also propose several variations of skyline computation and demonstrate how BBS can be applied to these variations. Experimental results show that BBS outperforms existing algorithms in terms of efficiency and space consumption, making it a promising solution for skyline computation in large datasets.The article introduces the branch-and-bound skyline (BBS) algorithm, a novel approach to skyline computation in database systems. Skyline computation is crucial for multicriteria decision-making, and existing algorithms often have limitations in terms of performance and applicability. BBS is designed to be I/O optimal, meaning it only accesses nodes that may contain skyline points, and it supports progressive processing, allowing for efficient initial results without reading the entire dataset. The algorithm is based on nearest-neighbor search and is simple to implement, supporting various types of progressive processing. The authors also propose several variations of skyline computation and demonstrate how BBS can be applied to these variations. Experimental results show that BBS outperforms existing algorithms in terms of efficiency and space consumption, making it a promising solution for skyline computation in large datasets.