1. Reverse Furthest Neighbors Query in Road Networks
- Author
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Feilong Tang, Minyi Guo, Jingyu Zhou, Jin-Song Bao, Xiao-Jun Xu, Jianqiu Xu, and Bin Yao
- Subjects
Theoretical computer science ,Computer science ,02 engineering and technology ,Computer Science Applications ,Theoretical Computer Science ,Set (abstract data type) ,Combinatorics ,Computational Theory and Mathematics ,Hardware and Architecture ,020204 information systems ,Road networks ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Point (geometry) ,Software ,MathematicsofComputing_DISCRETEMATHEMATICS - Abstract
Given a road network G = (V,E), where V (E) denotes the set of vertices(edges) in G, a set of points of interest P and a query point q residing in G, the reverse furthest neighbors (RfnR) query in road networks fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in P ∪ {q}. This is the monochromatic RfnR (MrfnR) query. Another interesting version of RfnR query is the bichromatic reverse furthest neighbor (BrfnR) query. Given two sets of points P and Q, and a query point q ∈ Q, a BrfnR query fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in Q. This paper presents efficient algorithms for both MrfnR and BrfnR queries, which utilize landmarks and partitioning-based techniques. Experiments on real datasets confirm the efficiency and scalability of proposed algorithms.
- Published
- 2017
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