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How Reduce Side Join Part File Expressions Equal MapReduce Structure into Task Consequences, Performanceh
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 105 - Number 2 |
| Year of Publication: 2014 |
| Authors: Ravi (ravinder) Prakash G, Kiran M |
10.5120/18347-9468
|
Ravi (ravinder) Prakash G, Kiran M . How Reduce Side Join Part File Expressions Equal MapReduce Structure into Task Consequences, Performanceh. International Journal of Computer Applications. 105, 2 ( November 2014), 8-15. DOI=10.5120/18347-9468
Abstract
An intention of MapReduce Sets for Reduce side join part file expressions analysis has to suggest criteria how Reduce side join part file expressions in Reduce side join part file data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for Reduce side join part file Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific Reduce side join part file process that are expressiveness of Reduce side join part file models and reproducibility of their Reduce side join part file inference. Reduce side join part file expressions are assumed to be elements of a Reduce side join part file expression space or Conjecture class and Reduce side join part file data provide "information" which of these Reduce side join part file expressions should be used to interpret the Reduce side join part file data. An inference Reduce side join part file algorithm constructs the mapping between Reduce side join part file data and Reduce side join part file expressions, in particular by a Reduce side join part file cost minimization process. Fluctuations in the Reduce side join part file data often limit the Reduce side join part file precision, which we can achieve to uniquely identify a single Reduce side join part file expression as interpretation of the Reduce side join part file data. We advocate an information theoretic perspective on Reduce side join part file expression analysis to resolve this dilemma where the tradeoff between Reduce side join part file informativeness of statistical inference Reduce side join part file and their Reduce side join part file stability is mirrored in the information-theoretic Reduce side join part file optimum of high Reduce side join part file information rate and zero communication expression error. The inference Reduce side join part file algorithm is considered as an outlier object Reduce side join part file path, which naturally limits the resolution of the Reduce side join part file expression space given the uncertainty of the Reduce side join part file 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.
- 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.
- Ravi (Ravinder) Prakash G and Kiran M. , How Minimal are MapReduce Arrangements for Binning Expressions. International Journal of Computer Applications Volume 99 (11): 7-14, August 2014
- Ravi (ravinder) Prakash G and Kiran M. , Shuffling Expressions with MapReduce Arrangements and the Role of Binary Path Symmetry. International Journal of Computer Applications 102 (16): 19-24, September 2014.
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