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A nested hierarchy of dynamically evolving clouds for big data structuring and searching

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A nested hierarchy of dynamically evolving clouds for big data structuring and searching. / Angelov, Plamen; Sadeghi Tehran, Pouria.

In: Procedia Computer Science, Vol. 53, 08.08.2015, p. 1-8.

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Angelov, Plamen ; Sadeghi Tehran, Pouria. / A nested hierarchy of dynamically evolving clouds for big data structuring and searching. In: Procedia Computer Science. 2015 ; Vol. 53. pp. 1-8.

Bibtex

@article{f9371db30eea4c43bc18517134b9d361,
title = "A nested hierarchy of dynamically evolving clouds for big data structuring and searching",
abstract = "The need to analyse big data streams and prescribe actions pro-actively is pervasive in nearlyevery industry. As growth of unstructured data increases, using analytical systems to assimilateand interpret images and videos as well as interpret structured data is essential. In this paper,we proposed a novel approach to transform image dataset into higher-level constructs thatcan be analysed more computationally efficiently, reliably and extremely fast. The proposedapproach provides a high visual quality result between the query image and data clouds withhierarchical dynamically nested evolving structure. The results illustrate that the introducedapproach can be an effective yet computationally efficient way to analyse and manipulate storedimageswhich has become the centre of attention of many professional fields and institutionalsectors over the last few years.",
keywords = "Evolving classifiers, data clouds, clustering",
author = "Plamen Angelov and {Sadeghi Tehran}, Pouria",
year = "2015",
month = aug
day = "8",
doi = "10.1016/j.procs.2015.07.273",
language = "English",
volume = "53",
pages = "1--8",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - A nested hierarchy of dynamically evolving clouds for big data structuring and searching

AU - Angelov, Plamen

AU - Sadeghi Tehran, Pouria

PY - 2015/8/8

Y1 - 2015/8/8

N2 - The need to analyse big data streams and prescribe actions pro-actively is pervasive in nearlyevery industry. As growth of unstructured data increases, using analytical systems to assimilateand interpret images and videos as well as interpret structured data is essential. In this paper,we proposed a novel approach to transform image dataset into higher-level constructs thatcan be analysed more computationally efficiently, reliably and extremely fast. The proposedapproach provides a high visual quality result between the query image and data clouds withhierarchical dynamically nested evolving structure. The results illustrate that the introducedapproach can be an effective yet computationally efficient way to analyse and manipulate storedimageswhich has become the centre of attention of many professional fields and institutionalsectors over the last few years.

AB - The need to analyse big data streams and prescribe actions pro-actively is pervasive in nearlyevery industry. As growth of unstructured data increases, using analytical systems to assimilateand interpret images and videos as well as interpret structured data is essential. In this paper,we proposed a novel approach to transform image dataset into higher-level constructs thatcan be analysed more computationally efficiently, reliably and extremely fast. The proposedapproach provides a high visual quality result between the query image and data clouds withhierarchical dynamically nested evolving structure. The results illustrate that the introducedapproach can be an effective yet computationally efficient way to analyse and manipulate storedimageswhich has become the centre of attention of many professional fields and institutionalsectors over the last few years.

KW - Evolving classifiers

KW - data clouds

KW - clustering

U2 - 10.1016/j.procs.2015.07.273

DO - 10.1016/j.procs.2015.07.273

M3 - Journal article

VL - 53

SP - 1

EP - 8

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

ER -