An optimal and progressive algorithm for skyline queries

Abstract

The skyline of a set of d-dimensional points contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive (or online) algorithms that can quickly return the first skyline points without having to read the entire data file. Currently, the most efficient algorithm is NN (nearest neighbors), which applies the divideand-conquer framework on datasets indexed by R-trees. Although NN has some desirable features (such as high speed for returning the initial skyline points, applicability to arbitrary data distributions and dimensions), it also presents several inherent disadvantages (need for duplicate elimination if d>2, multiple accesses of the same node, large space overhead). In this paper we develop BBS (branch-and-bound skyline), a progressive algorithm also based on nearest neighbor search, which is IO optimal, i.e., it performs a single access only to those R-tree nodes that may contain skyline points. Furthermore, it does not retrieve duplicates and its space overhead is significantly smaller than that of NN. Finally, BBS is simple to implement and can be efficiently applied to a variety of alternative skyline queries. An analytical and experimental comparison shows that BBS outperforms NN (usually by orders of magnitude) under all problem instances.

Efficient Distributed Skylining for Web Information Systems

“…Initially skyline queries were mainly intended to be performed within a single database query engine. Thus the first algorithms and subsequent improvements all work on a central (multidimensional) index structure like R*-trees [20], certain partitioning schemes [21] or k-nearest-neighbor searches [13]. However, such central indexes cannot be applied to distributed Web information systems.…”

Dynamic Skyline Queries in Large Graphs

“…Kossmann et al propose Nearest Neighbor (NN) method to process skyline queries progressively [11]. Papadias et al introduce another efficient progressive algorithm named Branchand-Bound Skyline (BBS) [13]. Recently, Lee et al [12] propose a new method to answer skyline queries.…”

“…Recently, Lee et al [12] propose a new method to answer skyline queries. The most related works to ours are dynamic skyline [13], spatial skyline [16], multi-source skyline on the road networks [6], and dynamic skyline queries in metric space [4]. Papadias et al [13] first introduce dynamic skyline problems.…”

“…The most related works to ours are dynamic skyline [13], spatial skyline [16], multi-source skyline on the road networks [6], and dynamic skyline queries in metric space [4]. Papadias et al [13] first introduce dynamic skyline problems. Recently, Chen and Xiang have proposed MSQ algorithms for dynamic skyline problems [4], where the dimension functions can be any metric function.…”

Efficient Geometric Pruning Strategies for Continuous Skyline Queries

“…When the fighting player moves, she should keep her eyes on those guardians who are close and most dangerous to her in terms of energy, weapon, etc. To find suitable restaurants during travel or to escape guardians while moving to the player's destination, the intuitive approach to updating the skyline query results is to recalculate the skyline results using efficient algorithms from scratch, such as branch-and-bound skyline (BBS) [3,4]. However, the relatively effective solution is to cache the last computed skyline results and to only calculate those data objects that may enter or leave the skyline results.…”