کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4951200 1441194 2017 29 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Geographic spatiotemporal big data correlation analysis via the Hilbert-Huang transformation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Geographic spatiotemporal big data correlation analysis via the Hilbert-Huang transformation
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computer and System Sciences - Volume 89, November 2017, Pages 130-141
نویسندگان
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