Given a set of data elements 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. 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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Feng, X., Zhang, W., Zhao, X., Zhang, Y., & Gao, Y. (2013). Probabilistic k-skyband operator over sliding windows. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7923 LNCS, pp. 190–202). Springer Verlag. https://doi.org/10.1007/978-3-642-38562-9_20
Mendeley helps you to discover research relevant for your work.