Group visible nearest neighbor queries in spatial databases
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Continuous nearest-neighbor search in the presence of obstacles
ACM Transactions on Database Systems (TODS)
wNeighbors: a method for finding k nearest neighbors in weighted regions
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Continuous visible nearest neighbor query processing in spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
On efficient obstructed reverse nearest neighbor query processing
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
DART: an efficient method for direction-aware bichromatic reverse k nearest neighbor queries
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Reverse nearest neighbor (RNN) queries have a broad application base such as decision support, profile-based marketing, resource allocation, etc. Previous work on RNN search does not take obstacles into consideration. In the real world, however, there are many physical obstacles (e.g., buildings) and their presence may affect the visibility between objects. In this paper, we introduce a novel variant of RNN queries, namely, visible reverse nearest neighbor (VRNN) search, which considers the impact of obstacles on the visibility of objects. Given a data set P, an obstacle set O, and a query point q in a 2D space, a VRNN query retrieves the points in P that have q as their visible nearest neighbor. We propose an efficient algorithm for VRNN query processing, assuming that P and O are indexed by R-trees. Our techniques do not require any preprocessing and employ half-plane property and visibility check to prune the search space. In addition, we extend our solution to several variations of VRNN queries, including: 1) visible reverse k-nearest neighbor (VRkNN) search, which finds the points in P that have q as one of their k visible nearest neighbors; 2) \delta-VRkNN search, which handles VRkNN retrieval with the maximum visible distance \delta constraint; and 3) constrained VRkNN (CVRkNN) search, which tackles the VRkNN query with region constraint. Extensive experiments on both real and synthetic data sets have been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.