Final published version, 248 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -