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Data Analyzing using Big Data (Hadoop) in Billing System

Raju Din1 , Prabadevi B.2

  1. School of Information Technology and Engineering, VIT University, Vellore, India.
  2. School of Information Technology and Engineering, VIT University, Vellore, India.

Correspondence should be addressed to: rajudintagala23@gmail.com .

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-5 , Page no. 84-88, May-2017

Online published on May 30, 2017

Copyright © Raju Din, Prabadevi B. . 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: Raju Din, Prabadevi B. , “Data Analyzing using Big Data (Hadoop) in Billing System,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.84-88, 2017.

MLA Style Citation: Raju Din, Prabadevi B. "Data Analyzing using Big Data (Hadoop) in Billing System." International Journal of Computer Sciences and Engineering 5.5 (2017): 84-88.

APA Style Citation: Raju Din, Prabadevi B. , (2017). Data Analyzing using Big Data (Hadoop) in Billing System. International Journal of Computer Sciences and Engineering, 5(5), 84-88.

BibTex Style Citation:
@article{Din_2017,
author = {Raju Din, Prabadevi B. },
title = {Data Analyzing using Big Data (Hadoop) in Billing System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2017},
volume = {5},
Issue = {5},
month = {5},
year = {2017},
issn = {2347-2693},
pages = {84-88},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1268},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1268
TI - Data Analyzing using Big Data (Hadoop) in Billing System
T2 - International Journal of Computer Sciences and Engineering
AU - Raju Din, Prabadevi B.
PY - 2017
DA - 2017/05/30
PB - IJCSE, Indore, INDIA
SP - 84-88
IS - 5
VL - 5
SN - 2347-2693
ER -

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Abstract

Hadoop is an open source structure in java that grants differing kind of immense datasets transversely over different groups of PCs using many programing models on which tremens -dous data works. By and large we saw that on the off chance that we increment the measure of the datasets away media, then recovering of information sets aside longer opportunity to prepare. Significant explanation behind this is because of the heap forced on information. So to take care of this kind of issues we utilize Big Data developed to fill this need. In this paper Hadoop eco-frameworks like Sqoop, hive, pig latin and so forth are utilized. Likewise we investigate expansive volume of power charging framework information and increased more prominent exactness in results, too it figures quick and furthermore recuperates loss of information.

Key-Words / Index Term

Sqoop,Hive,Pig,Hadoop,Volume

References

[1] R Granell, CJ Axon, DCH Wallom, "Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles”, IEEE Transactions on Power Systems, Vol.30, No.6, pp.3217-3224, 2015.
[2] Zhang, Pei, Xiaoyu Wu, Xiaojun Wang, Sheng Bi, "Short-term load forecasting based on big data technologies”, CSEE Journal of Power and Energy Systems, Vol.1, No.3, pp.59-67, 2015.
[3] N. Mahmoudi-Kohan, M. P. Moghaddam, M. K. Sheikh-El-Eslami, S.M. Bidaki, "Improving WFA k-means technique for demand response programs applications”, 2009 IEEE Power & Energy Society General Meeting, Calgary, pp.1-5, 2009.
[4] C León, F Biscarri, I Monedero, JI. Guerrero, J. Biscarri, R. Millán. "Variability and trend-based generalized rule induction model to NTL detection in power companies." IEEE Transactions on Power Systems, Vol.26, No.4, pp.1798-1807, 2011.
[5] Y. Yang, W. Zhiliang, Q. Zhang, Yang Yang, "A time based markov model for automatic position-dependent services in smart home”, In Control and Decision Conference (CCDC), Chinaese, pp. 2771-2776, 2010.
[6] J Kwac, J Flora, R Rajagopal, "Household energy consumption segmentation using hourly data", IEEE Transactions on Smart Grid, Vol. 5, No.1, pp.420-430, 2014.
[7] S. Haben, C. Singleton, G. Peter, "Analysis and clustering of residential customers energy behavioral demand using smart meter data", IEEE Transactions on Smart Grid, Vol.7, No.1, pp.136-144, 2016.