Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network

Shanliang Pan, Yihong Dong, Jinfeng Cao and Ken Chen

International Journal of Distributed Sensor Networks, 2014, vol. 10, issue 3, 365064

Abstract: In moving environment, the positions of moving objects cannot be located accurately. Apart from the measuring instrument errors, movement of the objects is the main factor contributing to this uncertainty. This uncertainty makes dominant relationship of data instable, which will affect skyline operator. In this paper, we mainly study the continuous probabilistic skyline query for uncertain moving objects in road network. The query point is deemed to be stationary while moving objects are treated as targets with uncertainty described by a probability density function. After defining the notion of dominant probability and probabilistic skyline, we put forward a novel algorithm to deal with continuous probabilistic skyline query on road network. Firstly, we compute the dominant probability and skyline probability to get initial permanent p -skyline set. Then we define events to predict the time when dominant relationship between moving objects may change. Furthermore, we track and calculate events to update the probabilistic skyline in an incremental way. Two pruning strategies are proposed to cancel invalid events and objects in a bid to diminish search space. Finally, an extensive experimental evaluation on real datasets shows that probabilistic skyline sets in road network can be updated by the proposed algorithm. It demonstrates both efficiency and effectiveness.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:10:y:2014:i:3:p:365064

DOI: 10.1155/2014/365064

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