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MGR: Efficiently Processing Maximal Group Reverse k Nearest Neighbors Queries
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AI
DOI：10.1109/ACCESS.2022.3188396.png)
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摘要
En 中文
Given a set of facilities and a set of clients, a reverse k nearest neighbors (Rk NN) query returns every client for which the query facility is one of the k-closest facilities. Rk NN query has been studied thoroughly for its importance in various fields such as facility location. In this paper, we propose a brand new variant of RkNN query, namely, maximal group reverse k nearest neighbors (MaxGroupRkNN) query. Given a set of clients, a set of candidate facilities and parameters k and m, MaxGroupRkNN query returns a set of m facilities out of the candidates, such that the total number of RkNNs of the result set is maximized. The MaxGroupRkNN query is important for multi-facility location problem, which aims to maximize the total potential clients of a group of facilities providing the same service such as chain stores, charging stations and logistic centers. A straightforward solution is to enumerate all possible combinations which is obviously time consuming. In order to address this problem, we present an effcient solution namely MGR, which is based on a well-designed pruning technique. The proposed pruning technique is able to filter out the candidates that cannot contribute to the final result and reduce the computation cost dramatically. Moreover, we propose a well-designed optimization technique that can further reduce the computation cost. A detailed theoretical analysis of our methods is provided and the experimental results also confirm that our proposed methods have high efficiency and scalability.
Keyword:
Spatial queries
reverse k nearest neighbors
facility location
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Voronoi-based reverse nearest neighbor query processing on spatial networks.png)
MULTIMEDIA SYSTEMS
MULTIMEDIA SYSTEMSIF3.1
PREAI
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