An Emergence Techniques In Big Data Mining

Authors(1) :-Hemant N. Randhir

The term Big Data describes innovative techniques and technologies to capture, store, distribute, manage and analyze petabyte- or larger-sized datasets with high-velocity and different structures. Big data can be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. Data is generated from various different sources and can arrive in the system at various rates. In order to process these large amounts of data in an inexpensive and efficient way, parallelism is used. Big Data is a data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. Hadoop is the core platform for structuring Big Data, and solves the problem of making it useful for analytics purposes. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance.

Authors and Affiliations

Hemant N. Randhir
Department of Information, RCPIT Shirpur, Maharashtra, India

Big Data, Hadoop, Map Reduce, HDFS, Hadoop Components

  1. S.Vikram Phaneendra & E.Madhusudhan Reddy “Big Data- solutions for RDBMS problems- A survey” In 12th IEEE/IFIP Network Operations & Management Symposium (NOMS 2010) (Osaka, Japan, Apr 19{23 2013).
  2. Kiran kumara Reddi & Dnvsl Indira “Different Technique to Transfer Big Data : survey” IEEE Transactions on 52(8) (Aug.2013) 2348 { 2355}
  3. Jimmy Lin “MapReduce Is Good Enough?” The control project. IEEE Computer 32 (2013).
  4. Umasri.M.L, Shyamalagowri.D ,Suresh Kumar.S “Mining Big Data:- Current status and forecast to the future” Volume 4, Issue 1, January 2014 ISSN: 2277 128X
  5. Albert Bifet “Mining Big Data In Real Time” Informatica 37 (2013) 15–20 DEC 2012
  6. Bernice Purcell “The emergence of “big data” technology and analytics” Journal of Technology Research 2013.
  7. Sameer Agarwal†, Barzan MozafariX, Aurojit Panda†, Henry Milner†, Samuel MaddenX, Ion Stoica “BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data” Copyright © 2013ì ACM 978-1-4503-1994 2/13/04
  8. Yingyi Bu _ Bill Howe _ Magdalena Balazinska _ Michael D. Ernst “The HaLoop Approach to Large-Scale Iterative Data Analysis” VLDB 2010 paper “HaLoop: Efficient Iterative Data Processing on Large Clusters.
  9. Shadi Ibrahim⋆ _ Hai Jin _ Lu Lu “Handling Partitioning Skew in MapReduce using LEEN” ACM 51 (2008) 107–113
  10. Kenn Slagter · Ching-Hsien Hsu “An improved partitioning mechanism for optimizing massive data analysis using MapReduce” Published online: 11 April 2013

Publication Details

Published in : Volume 2 | Issue 2 | March-April 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 450-455
Manuscript Number : CSEIT1722142
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Hemant N. Randhir, "An Emergence Techniques In Big Data Mining", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.450-455, March-April-2017.
Journal URL :

Article Preview

Follow Us

Contact Us