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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

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