Skyline queries have emerged as an increasingly popular tool for identifying a set of interesting objects that balance different user-specified criteria. Although in several applications the user aims to detect data objects that have values as good as possible in all specified criteria, skyline queries fail to identify only those objects. Instead, objects whose values are good in a subset of the given criteria are also included in the skyline set, even though they may take arbitrarily bad values in the remaining criteria. To alleviate this shortcoming, we study the decisive subspaces that express the semantics of skyline points and determine skyline membership. We propose a novel alternative query, called decisive skyline query, which retrieves a set of points that balance all specified criteria. We study two variants of the proposed query, the strict variant, which retrieves only the subset of skyline points that have the full data space as decisive subspace, and the relaxed variant, which imposes the decisive semantics in a more flexible way. Furthermore, we present pruning properties that accelerate the process of finding the decisive skyline set. Capitalizing on these pruning properties, we propose a novel efficient algorithm for computing decisive skyline points. Our experimental study, which employs both synthetic and real data sets for various experimental setups, demonstrates the efficiency and effectiveness of our algorithm, and shows that the newly proposed query is more intuitive and informative for the user.

Skyline 查询已成为一种越来越流行的工具，用于识别一组平衡不同用户指定标准的有趣对象。尽管在一些应用程序中，用户的目标是检测在所有指定标准中具有尽可能好的值的数据对象，但天际线查询无法仅识别这些对象。相反，在给定标准的子集中值良好的对象也包含在天际线集中，即使它们在其余标准中可能采用任意不良值。为了缓解这一缺点，我们研究了表达天际线点语义并确定天际线隶属度的决定性子空间。我们提出了一种新颖的替代查询，称为决定性天际线查询，它检索平衡所有指定条件的一组点。我们研究了所提出的查询的两种变体，严格变体，它仅检索将完整数据空间作为决定性子空间的天际线点的子集，以及宽松变体变体，它以更灵活的方式强加决定性的语义。此外，我们还提出了修剪特性，可以加速寻找决定性天际线集的过程。利用这些修剪特性，我们提出了一种新颖的有效算法来计算决定性的天际线点。我们的实验研究使用合成数据集和真实数据集进行各种实验设置，证明了我们算法的效率和有效性，并表明新提出的查询对于用户来说更加直观和信息丰富。