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A Novel Data AggregationTechnique for Removing Redundant Data in Hadoop

Uday Shankar S V1 , AnveshNaik 2 , Manoj C K3 , Praveen B4 , Yadush B R5

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-15 , Page no. 270-271, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si15.270271

Online published on May 16, 2019

Copyright © Uday Shankar S V, AnveshNaik, Manoj C K, Praveen B, Yadush B R . 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: Uday Shankar S V, AnveshNaik, Manoj C K, Praveen B, Yadush B R, “A Novel Data AggregationTechnique for Removing Redundant Data in Hadoop,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.15, pp.270-271, 2019.

MLA Style Citation: Uday Shankar S V, AnveshNaik, Manoj C K, Praveen B, Yadush B R "A Novel Data AggregationTechnique for Removing Redundant Data in Hadoop." International Journal of Computer Sciences and Engineering 07.15 (2019): 270-271.

APA Style Citation: Uday Shankar S V, AnveshNaik, Manoj C K, Praveen B, Yadush B R, (2019). A Novel Data AggregationTechnique for Removing Redundant Data in Hadoop. International Journal of Computer Sciences and Engineering, 07(15), 270-271.

BibTex Style Citation:
@article{V_2019,
author = {Uday Shankar S V, AnveshNaik, Manoj C K, Praveen B, Yadush B R},
title = {A Novel Data AggregationTechnique for Removing Redundant Data in Hadoop},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {15},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {270-271},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1242},
doi = {https://doi.org/10.26438/ijcse/v7i15.270271}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i15.270271}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1242
TI - A Novel Data AggregationTechnique for Removing Redundant Data in Hadoop
T2 - International Journal of Computer Sciences and Engineering
AU - Uday Shankar S V, AnveshNaik, Manoj C K, Praveen B, Yadush B R
PY - 2019
DA - 2019/05/16
PB - IJCSE, Indore, INDIA
SP - 270-271
IS - 15
VL - 07
SN - 2347-2693
ER -

           

Abstract

Hadoop is the software framework which was developed by Apache Software Foundation.Hadoop framework is written in java with purpose to handle large amount of data. Hadoop manages huge volume of data.Hadoop runs the task under the MapReduce algorithm. MapReduce is a programming model suitable for processing of huge data. MapRe¬duc¬e framework has two phase, map phase and reduce phase.a mapredce job is usually splits the input data set into independent chunks,which is done by map phase.the framework sorts the output of the map which are input to reduce framework. To running frequent itemset require more resource and time consuming. To overcome this problem here we implementing the nobel data aggregation technique.

Key-Words / Index Term

herewe are grouping the frequent itemsetand remove the redundant data

References

[1]. Y. Xun, J. Zhang, and X. Qin, “Fidoop: Parallel mining of frequent itemsets using mapreduce,” IEEE Transactions on Systems,Man ,and Cybernetics: Systems, doi: 10.1109/TSMC.2015.2437327, 2015.
[2]. J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of massive datasets. Cambridge University Press, 2014.
[3]. M. Liroz-Gistau, R. Akbarinia, D. Agrawal, E. Pacitti, and P. Valduriez,“Data partitioning for minimizing transferred data in mapreduce,” in Data Management in Cloud, Grid and P2P Systems. Springer,2013.
[4]. T. Kirsten, L. Kolb, M. Hartung, A. Groß, H. K¨opcke, and E. Rahm,“Data partitioning for parallel entity matching,” Proceedings of theVLDB Endowment, vol. 3, no. 2, 2010.