Skyline query processing for uncertain data

Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over un-certain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continu-ous 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 probabil-ity threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational over-head. Our framework employs an efficient two-phase query processing algorithm