Decisive skyline queries for truly balancing multiple criteria.

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.