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Geographic spatiotemporal big data correlation analysis via the Hilbert–Huang transformation

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Version 2 2024-06-04, 11:01
Version 1 2017-07-26, 15:38
journal contribution
posted on 2024-06-04, 11:01 authored by W Song, L Wang, Y Xiang, AY Zomaya
As a typical representative of big data, geographic spatiotemporal big data present new features especially the non-stationary feature, bringing new challenges to mine correlation information. However, representation of instantaneous information is the main bottleneck for non-stationary data, but the traditional non-stationary analysis methods are limited by Heisenberg's uncertainty principle. Therefore, we firstly represent instantaneous frequency of geographic spatiotemporal big data based on Hilbert–Huang transform to overcome traditional methods’ weakness. Secondly, we propose absolute entropy correlation analysis method based on KL divergence. Finally, we select five geographic factors to certify that the absolute entropy correlation analysis method is effective and distinguishable.

History

Journal

Journal of computer and system sciences

Volume

89

Pagination

130-141

Location

Amsterdam, The Netherlands

Open access

  • Yes

ISSN

0022-0000

eISSN

1090-2724

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2017, Elsevier

Publisher

Elsevier