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On Randomized Minimal MapReduce Sets for Filtering Expressions

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 98 - Number 3
Year of Publication: 2014
Authors:
Ravi (ravinder) Prakash G
Kiran M
10.5120/17160-7216

Ravi (ravinder) Prakash G and Kiran M. Article: On Randomized Minimal MapReduce Sets for Filtering Expressions. International Journal of Computer Applications 98(3):1-8, July 2014. Full text available. BibTeX

@article{key:article,
	author = {Ravi (ravinder) Prakash G and Kiran M},
	title = {Article: On Randomized Minimal MapReduce Sets for Filtering Expressions},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {98},
	number = {3},
	pages = {1-8},
	month = {July},
	note = {Full text available}
}

Abstract

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

References

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Berli'nska, J. , Drozdowski, M. : Scheduling divisible MapReduce computations. J. Parallel Distrib. Comput 71(3), 450-459 (2011).
  • 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).
  • 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.
  • 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.
  • 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.