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kNN Research based on Multi-Source Query Points on Road Networks

Volume 13, Number 4, July 2017 - Paper 17 - pp. 501-510
DOI: 10.23940/ijpe.17.04.p17.501510

Jia Liua,b, Wei Chena,b, Lin Zhaoa, Junfeng Zhoua, Ziyang Chena,c

aSchool of Information Science and Engineering, YanShan University, No.438, HebeiStreet West, Qinhuangdao and 066004, P.R.China
bDepartment of Information Engineering, Hebei University of Environmental Engineering, No.8, JingangRoad, Qinhuangdao and 066102, P.R.
cSchool of Information and Management, Shanghai Lixin University of Accounting and Finance, Shanghai and 201620, P.R.China

(Submitted on February 13, 2017; Revised on May 5, 2017; Accepted on June 25, 2017)

Abstract:

Given a query point set and an object point set, a multi-source query of k nearest neighbors (MQ-kNN) returns the query set for its k closest objects. However, most existing nearest neighbor query algorithms are based on a single-source query point (SQ-kNN), and the query point is often the user's location. However, in some cases, a query can be a point set. For example, a user wants to choose a house from the existing idle houses (query points) and hopes that its surrounding facilities (object points) are best. For this kind of application, we study the problem of MQ-kNN on road networks and try to solve MQ-kNN query problems. A basic algorithm based on Dijkstra algorithm is proposed as an original algorithm by calculating SQ-kNN repeatedly. Then, two improved algorithms are proposed by taking all query points as a whole and adopting the effective pruning strategy. Comprehensive experiments on five road network datasets clearly demonstrate the efficiency of this method.

 

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