Skyline query processing for uncertain data

Abstract

Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over uncertain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continuous uncertain data, and propose a novel, efficient framework to answer these queries. Query answers are probabilistic, where each object is associated with a probability value of being a query answer. Typically, users specify a probability threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational overhead. Our framework employs an efficient two-phase query processing algorithm.