Abstract

Given a set of data elements $\mathcal D$ in a d-dimensional space, a k-skyband query reports the set of elements which are dominated by at most k−1 other elements in $\mathcal D$. k-skyband query is a fundamental query type in data analyzing as it keeps a minimum candidate set for all top-k ranking queries where the ranking functions are monotonic. In this paper, we study the problem of k-skyband over uncertain data streams following the possible world semantics where each data element is associated with an occurrence probability. Firstly, a dynamic programming based algorithm is proposed to identify k-skyband results for a given set of uncertain elements regarding a pre-specified probability threshold. Secondly, we characterize the minimum set of elements to be kept in the sliding window to guarantee correct computing of k-skyband. Thirdly, efficient update techniques based on R-tree structures are developed to handle frequent updates of the elements over the sliding window. Extensive empirical studies demonstrate the efficiency and effectiveness of our techniques.

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