Probabilistic skyline queries on uncertain time series

The uncertainty of data is popular and inherent in most applications. Although skyline queries on time series in the interval has attracted great interest in recent years, skyline queries on uncertain time series remains an open problem so far.To handle this issue, we model the skyline queries on uncertain time series, and develop a two-step procedure to answer the probabilistic skyline queries on the dataset. First, three effective pruning techniques are proposed to obtain the skyline in the interval. Next, two simple methods are proposed to compute the skyline probability of each uncertain time series. For the online skyline queries, we also introduce a solution to improve the efficiency of pruning strategies by sharing the computation for two adjacent intervals. Experiments verify the effectiveness of probabilistic skylines and the efficiency and scalability of our algorithms.