Computing All Skyline Probabilities for Uncertain Data

Skyline computation is widely used in multi-criteria decision making. As research in uncertain databases draws increasing attention, skyline queries with uncertain data have also been studied, e.g. probabilistic skylines. The previous work requires "thresholding" for its efficiency - the efficiency relies on the assumption that points with skyline probabilities below a certain threshold can be ignored. But there are situations where "thresholding" is not desirable - low probability events cannot be ignored when their consequences are significant. In such cases it is necessary to compute skyline probabilities of all data items. We provide the first algorithm for this problem whose worst-case time complexity is sub-quadratic. The techniques we use are interesting in their own right, as they rely on a space partitioning technique combined with using the existing dominance counting algorithm. The effectiveness of our algorithm is experimental verified.