Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2018

Abstract

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.

Keywords

Algorithm, k nearest neighbor query, Metric space, Publish/subscribe, Query processing

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Web and big data: Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25: Proceedings

Volume

10987

First Page

236

Last Page

252

ISBN

9783319968902

Identifier

10.1007/978-3-319-96890-2_20

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1007/978-3-319-96890-2_20

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