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A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm

A. Patil1 , A. Palve2

  1. Dept. Of Computer Engineering, SITRC (Savitribai Phule Pune University), Nashik, India.
  2. Dept. Of Computer Engineering, SITRC (Savitribai Phule Pune University), Nashik, India.

Correspondence should be addressed to: ajit.patil1091@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 67-72, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.6772

Online published on Dec 31, 2017

Copyright © A. Patil, A. Palve . 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: A. Patil, A. Palve, “A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.67-72, 2017.

MLA Style Citation: A. Patil, A. Palve "A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm." International Journal of Computer Sciences and Engineering 5.12 (2017): 67-72.

APA Style Citation: A. Patil, A. Palve, (2017). A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm. International Journal of Computer Sciences and Engineering, 5(12), 67-72.

BibTex Style Citation:
@article{Patil_2017,
author = {A. Patil, A. Palve},
title = {A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2017},
volume = {5},
Issue = {12},
month = {12},
year = {2017},
issn = {2347-2693},
pages = {67-72},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1582},
doi = {https://doi.org/10.26438/ijcse/v5i12.6772}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i12.6772}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1582
TI - A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - A. Patil, A. Palve
PY - 2017
DA - 2017/12/31
PB - IJCSE, Indore, INDIA
SP - 67-72
IS - 12
VL - 5
SN - 2347-2693
ER -

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Abstract

In today’s digital world remote senses daily generate large amount of real time data know as Big-Data, wherever insight data encompasses a potential significance if collected and aggregative effectively. There is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a main computational disputes, such as to analyze, aggregate, & store, where data are remotely gathered. Keeping in view the above mentioned factors, there`s a requirement for planning a system design that welcomes each real-time, additionally as offline processing. In this paper we propose efficient and scalable solution to analyze pent bytes of data across an extremely wide increasing wealth of weather variables. In this research we are working on data analysis using Apache Hadoop and Java . Extensive experiments are carried out to find out the best tools among Distributed computing using Pig and Hive Queries. The proposed architecture has the potential of dividing, load balancing, & parallel processing of only utile data. Thus, it results in effectively analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Fixed width clustering algorithm is used to improve the accuracy of results.

Key-Words / Index Term

Big Data, data analysis decision unit (DADU), data processing unit (DPU), land and sea area, offline, real-time, remote senses, remote sensing Big Data acquisition unit (RSDU)

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