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Reseach Article

MRDS Data Processing and Mining using Hadoop in Cloud

by Ravindra P. Bachate, H. A. Hingoliwala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 10
Year of Publication: 2014
Authors: Ravindra P. Bachate, H. A. Hingoliwala
10.5120/15753-4260

Ravindra P. Bachate, H. A. Hingoliwala . MRDS Data Processing and Mining using Hadoop in Cloud. International Journal of Computer Applications. 90, 10 ( March 2014), 1-3. DOI=10.5120/15753-4260

@article{ 10.5120/15753-4260,
author = { Ravindra P. Bachate, H. A. Hingoliwala },
title = { MRDS Data Processing and Mining using Hadoop in Cloud },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 10 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number10/15753-4260/ },
doi = { 10.5120/15753-4260 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:38.677446+05:30
%A Ravindra P. Bachate
%A H. A. Hingoliwala
%T MRDS Data Processing and Mining using Hadoop in Cloud
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 10
%P 1-3
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This project explores the use of Hadoop framework for MRDS (Mineral Resources data system) data processing and mining in cloud. Cloud computing provides efficient computation and analysis for large data. To improve the performance of system for massive data, Hadoop provides Map Reduce technique. Hadoop has a distributed file system (HDFS) that stores data on the cluster nodes. This project focuses on to provide real time information of mineral resources stored in cloud environment with minimum data processing time. Storing MRDS data in to the cloud ensures the availability and reliability of it.

References
  1. Hongyong Yu, Deshuai Wang, "Mass Log Data Processing and Mining Based on Hadoop and Cloud Computing" . The 7th International Conference on Computer Science & Education (ICCSE 2012)July 14-17, 2012. Melbourne, Australia.
  2. Duck-Ho Bae Coll. of Inf. & Commun. , Hanyang Univ. , Seoul, South Korea Ji-Haeng Baek ; Hyun-Kyo Oh ; Ju-Won Song ; Sang-Wook Kim, "SD-Miner: A SPATIAL DATA MINING SYSTEM" Network Infrastructure and Digital Content, 2009.
  3. Weikuan Yu, Member, IEEE, Yandong Wang, and Xinyu Que, " Design and Evaluation of Network-Levitated Merge for Hadoop Acceleration", IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS.
  4. Xindong Wu,Fellow, IEEE,Xingquan Zhu, Senior Member, IEEE, Gong-Qing Wu, and Wei Ding,Senior Member, IEEE, "Data mining with big data," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 1, JANUARY 2014.
  5. Hadoop: The definitive Guide, 3rd ed. , O'Reilly, Tom White, 2012
  6. Hadoop in Action, Manning, Chuck Lam, 2011
  7. Hadoop, http://hadoop. apache. org/
  8. MRDS, http://tin. er. usgs. gov/mrds/
Index Terms

Computer Science
Information Sciences

Keywords

Hadoop cloud computing data processing data mining