Group k- Nearest Neighbor Query Method for Uncertainty Data in Obstructed Spaces

Group k- Nearest Neighbor Query Method for Uncertainty Data in Obstructed Spaces

To deal with the problem of group k nearest neighbor query method for uncertainty data in obstructed spaces, this paper presents the method of the PkOGNN（probabilistic k obstructed group nearest neighbor）query .The PkOGNN query method mainly includes four sub-algorithms: Compadist_o()，SpatialPru(...

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Bibliographic Details
Main Authors: WAN Jing, TANG Bei-bei, SUN Jian, HE Yun-bin, LI Song
Format: Article
Language:zho
Published: Harbin University of Science and Technology Publications 2019-06-01
Series:Journal of Harbin University of Science and Technology
Subjects:
r-tree
group nearest neighbor query
uncertainty
visibility
obstructed distance
Online Access:https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1678
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Summary:To deal with the problem of group k nearest neighbor query method for uncertainty data in obstructed spaces, this paper presents the method of the PkOGNN（probabilistic k obstructed group nearest neighbor）query .The PkOGNN query method mainly includes four sub-algorithms: Compadist_o()，SpatialPru()，PruInterEnt() and PkOGNN(), These algorithms are respectively the calculation of the aggregate obstructed distance, the spatial pruning method, the pruning of the R-tree intermediate items according to the spatial pruning method, the final refined query method. It integrates the effective pruning methods to reduce the search space of PkOGNN and get the correct kGNNs.The theoretical research and experimental results show that the proposed method has good efficiency.
ISSN:1007-2683

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