Nonlinear Phenomena in Complex Systems, 2020, Vol.23, No.3, pp.262

NONLINEAR PHENOMENA IN COMPLEX SYSTEMS
An Interdisciplinary Journal

2020, Vol.23, No.3, pp.262 - 269

Multi-Dimensional Data Aggregation in the Analysis of Self-Similar Processes
M. Poltavtseva and T. Andreeva

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.

Key words: mathematics for engineering, trees, directed graphs, self-similar processes, data analysis, big data, data aggregation, hierarchical aggregation, multidimensional aggregation

DOI: https://doi.org/10.33581/1561-4085-2020-23-3-262-269

Full text: Acrobat PDF (528 KB) Open Access