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Shuffling Expressions with MapReduce Arrangements and the Role of Binary Path Symmetry

by Ravi (ravinder) Prakash G, Kiran M
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 16
Year of Publication: 2014
Authors: Ravi (ravinder) Prakash G, Kiran M
10.5120/17900-8895

Ravi (ravinder) Prakash G, Kiran M . Shuffling Expressions with MapReduce Arrangements and the Role of Binary Path Symmetry. International Journal of Computer Applications. 102, 16 ( September 2014), 23-30. DOI=10.5120/17900-8895

@article{ 10.5120/17900-8895,
author = { Ravi (ravinder) Prakash G, Kiran M },
title = { Shuffling Expressions with MapReduce Arrangements and the Role of Binary Path Symmetry },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 16 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number16/17900-8895/ },
doi = { 10.5120/17900-8895 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:17.452342+05:30
%A Ravi (ravinder) Prakash G
%A Kiran M
%T Shuffling Expressions with MapReduce Arrangements and the Role of Binary Path Symmetry
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 16
%P 23-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An intention of MapReduce Sets for Shuffling expressions analysis has to suggest criteria how Shuffling expressions in Shuffling data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for Shuffling Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific Shuffling process that are expressiveness of Shuffling models and reproducibility of their Shuffling inference. Shuffling expressions are assumed to be elements of a Shuffling expression space or Conjecture class and Shuffling data provide "information" which of these Shuffling expressions should be used to interpret the Shuffling data. An inference Shuffling algorithm constructs the mapping between Shuffling data and Shuffling expressions, in particular by a Shuffling cost minimization process. Fluctuations in the Shuffling data often limit the Shuffling precision, which we can achieve to uniquely identify a single Shuffling expression as interpretation of the Shuffling data. We advocate an information theoretic perspective on Shuffling expression analysis to resolve this dilemma where the tradeoff between Shuffling informativeness of statistical inference Shuffling and their Shuffling stability is mirrored in the information-theoretic Shuffling optimum of high Shuffling information rate and zero communication expression error. The inference Shuffling algorithm is considered as an outlier object Shuffling path, which naturally limits the resolution of the Shuffling expression space given the uncertainty of the Shuffling data.

References
  1. Ravi Prakash G, Kiran M, and Saikat Mukherjee, Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model, International Journal of Computer Applications, Volume 86 – No 10, Page 30-34, January 2014.
  2. Kiran M. , Saikat Mukherjee and Ravi Prakash G. , Characterization of Randomized Shuffle and Sort Quantifiability in MapReduce Model, International Journal of Computer Applications, 51-58, Volume 79, No. 5, October 2013.
  3. Amresh Kumar, Kiran M. , Saikat Mukherjee and Ravi Prakash G. , Verification and Validation of MapReduce Program model for Parallel K-Means algorithm on Hadoop Cluster, International Journal of Computer Applications, 48-55, Volume 72, No. 8, June 2013.
  4. Kiran M. , Amresh Kumar, Saikat Mukherjee and Ravi Prakash G. , Verification and Validation of MapReduce Program Model for Parallel Support Vector Machine Algorithm on Hadoop Cluster, International Journal of Computer Science Issues, 317-325, Vol. 10, Issue 3, No. 1, May 2013.
  5. Aniruddha Basak, Irina Brinster and Ole J. Mengshoel. MapReduce for Bayesian Network Parameter Learning using the EM Algorithm, Proc. of Big Learning: Algorithms, Systems and Tools, 1-6, December 2012.
  6. Berli'nska, J. , Drozdowski, M. : Scheduling divisible MapReduce computations. J. Parallel Distrib. Comput 71(3), 450-459 (2011).
  7. Emanuel Vianna, Giovanni Comarela, Tatiana Pontes, Jussara Almeida, Virgilio Almeida, Kevin Wilkinson, Harumi Kuno, Umeshwar Dayal. Analytical Performance Models for MapReduce Workloads, Int J Parallel Prog 41:495-525 (2013).
  8. Erik B. Reed and Ole J. Mengshoel. Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce, Proc. of Big Learning: Algorithms, Systems and Tools, 1-5, December 2012.
  9. Ravi Prakash G, Kiran M and Saikat Mukherjee, On Randomized Preference Limitation Protocol for Quantifiable Shuffle and Sort Behavioral Implications in MapReduce Programming Model, Parallel & Cloud Computing, Vol. 3, Issue 1, 1-14, January 2014.
  10. Ravi Prakash G, and Kiran M, On The Least Economical MapReduce Sets for Summarization Expressions, International Journal of Computer Applications, 13-20, Volume 94, No. 7, May 2014.
  11. Ravi (Ravinder) Prakash G, Kiran M. , On Randomized Minimal MapReduce Sets for Filtering Expressions, International Journal of Computer Applications, Volume 98, No. 3, Pages 1-8, July 2014.
  12. Ravi (Ravinder) Prakash G and Kiran M. , How Minimal are MapReduce Arrangements for Binning Expressionsh. International Journal of Computer Applications Volume 99, No. 11, Pages: 7-14, August 2014.
Index Terms

Computer Science
Information Sciences

Keywords

MapReduce Shuffling expressions kernel function.