Efficient aggregate farthest neighbour query processing on road networks

Wang, Haozhou, Zheng, Kai, Su, Han, Wang, Jiping, Sadiq, Shazia and Zhou, Xiaofang (2014). Efficient aggregate farthest neighbour query processing on road networks. In: Hua Wang and Mohamed A. Sharaf, Databases Theory and Applications - 25th Australasian Database Conference, ADC 2014, Proceedings. 25th Australasian Database Conference, ADC 2014, Brisbane, QLD Australia, (13-25). 14 - 16 July 2014. doi:10.1007/978-3-319-08608-8_2


Author Wang, Haozhou
Zheng, Kai
Su, Han
Wang, Jiping
Sadiq, Shazia
Zhou, Xiaofang
Title of paper Efficient aggregate farthest neighbour query processing on road networks
Conference name 25th Australasian Database Conference, ADC 2014
Conference location Brisbane, QLD Australia
Conference dates 14 - 16 July 2014
Proceedings title Databases Theory and Applications - 25th Australasian Database Conference, ADC 2014, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Heidelberg, Germany
Publisher Springer Verlag
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-08608-8_2
Open Access Status
ISBN 9783319086071
9783319086088
ISSN 0302-9743
1611-3349
Editor Hua Wang
Mohamed A. Sharaf
Volume 8506 LNCS
Start page 13
End page 25
Total pages 13
Chapter number 2
Total chapters 21
Collection year 2015
Language eng
Abstract/Summary This paper addresses the problem of searching the k aggregate farthest neighbours (AkFN query in short) on road networks. Given a query point set, AkFN is aimed at finding the top-k points from a dataset with the largest aggregate network distance. The challenge of the AkFN query on the road network is how to reduce the number of network distance evaluation which is an expensive operation. In our work, we propose a three-phase solution, including clustering points in dataset, network distance bound pre-computing and searching. By organizing the objects into compact clusters and pre-calculating the network distance bound from clusters to a set of reference points, we can effectively prune a large fraction of clusters without probing each individual point inside. Finally, we demonstrate the efficiency of our proposed approaches by extensive experiments on a real Point- of-Interest (POI) dataset.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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