Application of decomposition algorithms to speed up processing of large data sets in GIS / Kotuliak, / Khilenko, / Basarab, / Ries. (2022)
Ukrainian

English  Cybernetics and Systems Analysis   /     Issue (2022, 58 (6))

Kotuliak I., Khilenko V.V., Basarab R.M., Ries M.
Application of decomposition algorithms to speed up processing of large data sets in GIS

The technology and decomposition algorithm for accelerating the processing of geoinformation data based on the distribution of samples of dynamic and quasi-static data using the analysis of eigenvalues of matrices obtained by means of iterative calculation according to the Khilenko’s method are proposed. The algorithm is aimed at processing large geoinformation data arrays. Comparative results of model calculations using known computation methods are given. © 2023, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: decomposition, geoinformation data, iterative calculation of matrix eigenvalues, Khilenko’s method, satellite image processing, Data handling, Geographic information systems, Image processing, Iterative methods, Matrix algebra, Satellite imagery, Data array, Decomposition algorithm, Geo-information, Geoinformation data, Iterative calculation, Iterative calculation of matrix eigenvalue, Khilenko’s method, Matrix eigenvalues, S-method, Satellite image processing, Eigenvalues and eigenfunctions


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Cite:
Kotuliak I., Khilenko V.V., Basarab R.M., Ries M. (2022). Application of decomposition algorithms to speed up processing of large data sets in GIS. Cybernetics and Systems Analysis, 58 (6), 106–113. doi: https://doi.org/10.1007/s10559-023-00528-8 http://jnas.nbuv.gov.ua/article/UJRN-0001368554 [In Ukrainian].


 

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