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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Main Authors: Enaleev A.K., Ciganov Vladimir V.
Format: Article
Language:English
Published: University of Belgrade - Faculty of Mechanical Engineering, Belgrade 2019-01-01
Series:FME Transactions
Subjects:
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.
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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