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Graph Analysis with Big-data

Kalpana 1

Section:Review Paper, Product Type: Journal Paper
Volume-3 , Issue-12 , Page no. 79-81, Dec-2015

Online published on Dec 31, 2015

Copyright © Kalpana . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kalpana, “Graph Analysis with Big-data,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.79-81, 2015.

MLA Style Citation: Kalpana "Graph Analysis with Big-data." International Journal of Computer Sciences and Engineering 3.12 (2015): 79-81.

APA Style Citation: Kalpana, (2015). Graph Analysis with Big-data. International Journal of Computer Sciences and Engineering, 3(12), 79-81.

BibTex Style Citation:
@article{_2015,
author = {Kalpana},
title = {Graph Analysis with Big-data},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2015},
volume = {3},
Issue = {12},
month = {12},
year = {2015},
issn = {2347-2693},
pages = {79-81},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=760},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=760
TI - Graph Analysis with Big-data
T2 - International Journal of Computer Sciences and Engineering
AU - Kalpana
PY - 2015
DA - 2015/12/31
PB - IJCSE, Indore, INDIA
SP - 79-81
IS - 12
VL - 3
SN - 2347-2693
ER -

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Abstract

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model from the data mining perspective. The planning of several optimal tuning processes, the comparison of different designs (through graphics or the numeric results obtained), and the management of data files saved during the planned optimal tunings process. The developed tool was made available to students for them to solve a practical problem and, subsequently, the impact of its use was evaluated. There are techniques to learn the categories (clustering). Methods of pattern recognition are useful in many applications such as information retrieval, data mining.

Key-Words / Index Term

MIMO,NLP,RGA,PIP

References

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