147 0

Parallel computation of k-nearest neighbor joins using MapReduce

Title
Parallel computation of k-nearest neighbor joins using MapReduce
Author
김영훈
Keywords
kNN joins; MapReduce; Hadoop
Issue Date
2016-12
Publisher
IEEE
Citation
2016 IEEE International Conference on Big Data (Big Data), Page. 696-705
Abstract
The k-nearest neighbor (kNN) join has recently attracted considerable attention due to its broad applications. However, processing fcNN joins is very expensive due to the quadratic nature of the join operation. Furthermore, since there is an increasing trend of applications to deal with big data, computing fcNN joins becomes more challenging. In order to process such big data, parallel and distributed computing using MapReduce recently have received a lot of attention. In this paper, we propose the efficient parallel algorithm KNN-MR to process the fcNN joins using MapReduce. To reduce not only the computational cost of fcNN joins but also the network cost of communicating across machines, we develop the novel vector projection pruning which enables us to identify non-fcNN points that are guaranteed not to be included in the result of a fcNN join. Our performance study confirms the effectiveness and scalability of the proposed algorithm.
URI
https://ieeexplore.ieee.org/document/7840662/http://repository.hanyang.ac.kr/handle/20.500.11754/103069
ISBN
978-1-4673-9005-7; 978-1-4673-9006-4
DOI
10.1109/BigData.2016.7840662
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE