Updating Queries for Probabilistic Skyline Set of Uncertain Moving Objects
Cao Jin-Feng · 2012
Recently, researchers pay more attention on dynamic and uncertain datasets instead of static objects in Skyline queries field. Aiming at the circumstances that the query point is fixed and target objects are moving with location uncertainty under moving environment, this paper retrieves continuous probability Skyline computation. The distances between moving objects and query point are variable with time continuously. Due to the uncertainty on location, the dominant relationship between moving objects is represented with probability and is constantly variable with time. Firstly, this paper defines the dominated probability and Skyline probability of moving objects. Then, it defines triggered events, which record the time of dominant probability changing, to track and update probabilistic Skyline computation continuously. It also proposes an algorithm of event triggered continuous probabilistic Skyline query for uncertain moving object (U-ECPS) to update the Skyline set. Finally, comprehensive experiments are conducted to demonstrate the efficiency of the proposed algorithm.