Efficient Probabilistic Skyline Computation Against n-of-N Data Stream Model

This paper studies the problem of computing q-skylines against probabilistic data streams.Compared with the existing methods,which only support the sliding window model,this method can support the more general n-of-N data stream model.This method of transforming q-skyline queries is used for the stabbing queries on an interval tree to support n-of-N model.The paper proposes an algorithm,named PnNM,to maintain the data structures,which is needed for supporting n-of-N model.The PnNM algorithm can efficiently handle the update of the candidate set of uncertain data objects and the updates of the intervals.An algorithm,named PnNCont,is also proposed to handle continuous q-skyline queries against n-of-N model.The theoretical analyses and extensive experiments demonstrate that this algorithms can be very efficient in handing q-skyline queries against probabilistic data streams under n-of-N model.