Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2019

Abstract

Similarity queries, including range queries and k nearest neighbor (kNN) queries, in metric spaces 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), to support efficient metric similarity queries in the distributed environment. AMDS uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronous process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In addition, we develop efficient similarity search algorithms using AMDS. Extensive experiments using real and synthetic data demonstrate the performance of metric similarity queries using AMDS. Moreover, the AMDS scales sublinearly with the growing data size.

Keywords

Similarity query, Metric space, Range query, Algorithm, kNN query, Distributed processing

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Data Science and Engineering

Volume

4

Issue

2

First Page

93

Last Page

108

ISSN

2364-1185

Identifier

10.1007/s41019-019-0095-7

Publisher

SpringerOpen (part of Springer Nature)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s41019-019-0095-7

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