Alignment of cluster complexity at network systems
This paper considers data management structures and cluster technologies in large-scale networks. Suboptimal network partitioning problems are formulated on the base of complexity index alignment. We propose methods for these problems solving, in particular the data clusters number and its boundarie...
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Format: | Article |
Language: | English |
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University of Belgrade - Faculty of Mechanical Engineering, Belgrade
2019-01-01
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Series: | FME Transactions |
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Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2019/1451-20921904711E.pdf |
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author | Enaleev A.K. Ciganov Vladimir V. |
author_facet | Enaleev A.K. Ciganov Vladimir V. |
author_sort | Enaleev A.K. |
collection | DOAJ |
description | This paper considers data management structures and cluster technologies in large-scale networks. Suboptimal network partitioning problems are formulated on the base of complexity index alignment. We propose methods for these problems solving, in particular the data clusters number and its boundaries determining. We describe a multi-stage iterative scheme for the semantic data mining from a large document with interdependent sections as well. At the first stage, a priori data mining complexity from these sections is estimated. Then we refine this complexity taking into account the revealed data mining from the adjacent sections. Based on this, the final partitioning of the data set of a big document into clusters is formed under circumstances of deadline and restrictions on financial resources. The proposed methods have been applied in some large-scale transport projects. |
first_indexed | 2024-12-10T06:37:33Z |
format | Article |
id | doaj.art-65fd94fea65e4a97a52a8edd565ba81f |
institution | Directory Open Access Journal |
issn | 1451-2092 2406-128X |
language | English |
last_indexed | 2024-12-10T06:37:33Z |
publishDate | 2019-01-01 |
publisher | University of Belgrade - Faculty of Mechanical Engineering, Belgrade |
record_format | Article |
series | FME Transactions |
spelling | doaj.art-65fd94fea65e4a97a52a8edd565ba81f2022-12-22T01:58:52ZengUniversity of Belgrade - Faculty of Mechanical Engineering, BelgradeFME Transactions1451-20922406-128X2019-01-014747117221451-20921904711EAlignment of cluster complexity at network systemsEnaleev A.K.0Ciganov Vladimir V.1Russian Academy Sciences, V.A. Trapeznikov Institute of Control Sciences, Moscow, RussiaRussian Academy Sciences, V.A. Trapeznikov Institute of Control Sciences, Moscow, RussiaThis paper considers data management structures and cluster technologies in large-scale networks. Suboptimal network partitioning problems are formulated on the base of complexity index alignment. We propose methods for these problems solving, in particular the data clusters number and its boundaries determining. We describe a multi-stage iterative scheme for the semantic data mining from a large document with interdependent sections as well. At the first stage, a priori data mining complexity from these sections is estimated. Then we refine this complexity taking into account the revealed data mining from the adjacent sections. Based on this, the final partitioning of the data set of a big document into clusters is formed under circumstances of deadline and restrictions on financial resources. The proposed methods have been applied in some large-scale transport projects.https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2019/1451-20921904711E.pdfbig datadata miningalignmentclusternetwork partitioningnp-hardnessheuristics |
spellingShingle | Enaleev A.K. Ciganov Vladimir V. Alignment of cluster complexity at network systems FME Transactions big data data mining alignment cluster network partitioning np-hardness heuristics |
title | Alignment of cluster complexity at network systems |
title_full | Alignment of cluster complexity at network systems |
title_fullStr | Alignment of cluster complexity at network systems |
title_full_unstemmed | Alignment of cluster complexity at network systems |
title_short | Alignment of cluster complexity at network systems |
title_sort | alignment of cluster complexity at network systems |
topic | big data data mining alignment cluster network partitioning np-hardness heuristics |
url | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2019/1451-20921904711E.pdf |
work_keys_str_mv | AT enaleevak alignmentofclustercomplexityatnetworksystems AT ciganovvladimirv alignmentofclustercomplexityatnetworksystems |