Skyline analysis is a key in a wide spectrum of real applications involving multi-criteria optimal decision making. In recent years, a considerable amount of research has been contributed on efficient computation of skyline probabilities over uncertain environment. Most studies if not all, assume uncertainty lies only in attribute values. To the extent of our knowledge, only one study addresses the skyline probability computation problem in scenarios where uncertainty resides in attribute preferences, instead of values. However this study takes a problematic approach by assuming independent object dominance, which we find is not always true in uncertain preference scenarios. In fact this assumption has already been shown to be not necessarily true in uncertain value scenarios. Motivated by this, we revisit the skyline probability computation over uncertain preferences in this paper.We first show that the problem of skyline probability computation over uncertain preferences is P-complete. Then we propose efficient exact and approximate algorithms to tackle this problem. While the exact algorithm remains exponential in the worst case, our experiments demonstrate its efficiency in practice. The approximate algorithm achieves -approximation by the confidence (1 − δ) with time complexity O(dn 1 2 ln 1 δ ), where n is the number of objects and d is the dimensionality. The efficiency and effectiveness of our methods are verified by extensive experimental results on real and synthetic data sets.

We demonstrated that using trend pattern features in prediction methods improved the accuracy of stroke outcome prediction. Therefore, trend patterns of physiological time series data have an important role in the early treatment of patients with acute ischaemic stroke.

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