Please use this identifier to cite or link to this item:
https://elib.bsu.by/handle/123456789/270180
Title: | Multi-Dimensional Data Aggregation in the Analysis of Self-Similar Processes |
Authors: | Poltavtseva, M. Andreeva, T. |
Keywords: | ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Физика |
Issue Date: | 2020 |
Publisher: | Minsk : Education and Upbringing |
Citation: | Nonlinear Phenomena in Complex Systems. - 2020. - Vol. 23, N 3. - P. 262-269 |
Abstract: | Analyzing self-similar processes in various fields requires fast and efficient processing of large amounts of data. The frequency and time scalability of self-similar processes require analysis over multiple time periods. Thus it is necessary to develop effective methods of data aggregation. The paper considers the hierarchical organization of time series and multidimensional aggregation based on a graph. The effectiveness of the proposed aggregation methods and their applicability to the analysis of self-similar processes in various fields are evaluated. |
URI: | https://elib.bsu.by/handle/123456789/270180 |
ISBN: | 10.33581/1561-4085-2020-23-3-262-269 |
ISSN: | 1561-4085 |
Licence: | info:eu-repo/semantics/restrictedAccess |
Appears in Collections: | 2020. Volume 23. Number 3 |
Files in This Item:
File | Description | Size | Format | |
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v23no3p262.pdf | 527,41 kB | Adobe PDF | View/Open |
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