Distributed k-Nearest Neighbor Queries in Metric Spaces

Xin Ding, Yuanliang Zhang, Lu Chen, Yujun Gao, Baihua Zheng

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2 Citationer (Scopus)

Abstrakt

Metric k nearest neighbor (MkNN) queries have applications in many areas such as multimedia retrieval, computational biology, and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), which uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronously process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In addition, we develop an efficient estimation based MkNN method using AMDS to improve the query efficiency. Extensive experiments using real and synthetic data demonstrate the performance of MkNN using AMDS. Moreover, the AMDS scales sub-linearly with the growing data size.
OriginalsprogEngelsk
TitelWeb and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
RedaktørerJianliang Xu, Yoshiharu Ishikawa, Yi Cai
Antal sider17
Vol/bind1
ForlagSpringer
Publikationsdato2018
Sider236-252
ISBN (Elektronisk)978-3-319-96890-2
DOI
StatusUdgivet - 2018
BegivenhedSecond International Joint Conference, APWeb-WAIM 2018 - Macau, Kina
Varighed: 23 jul. 201825 jul. 2018

Konference

KonferenceSecond International Joint Conference, APWeb-WAIM 2018
LandKina
ByMacau
Periode23/07/201825/07/2018
NavnLecture Notes in Computer Science
Vol/bind10987
ISSN0302-9743

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Citationsformater

Ding, X., Zhang, Y., Chen, L., Gao, Y., & Zheng, B. (2018). Distributed k-Nearest Neighbor Queries in Metric Spaces. I J. Xu, Y. Ishikawa, & Y. Cai (red.), Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings (Bind 1, s. 236-252). Springer. Lecture Notes in Computer Science, Bind. 10987 https://doi.org/10.1007/978-3-319-96890-2_20