The week's pick
Reseach Article
How Replicated Join Expressions Equal Map Phase or Reduce Phase in a MapReduce Structureh
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 107 - Number 12 |
| Year of Publication: 2014 |
| Authors: Ravi (ravinder) Prakash G, Kiran M |
10.5120/18807-0431
|
Ravi (ravinder) Prakash G, Kiran M . How Replicated Join Expressions Equal Map Phase or Reduce Phase in a MapReduce Structureh. International Journal of Computer Applications. 107, 12 ( December 2014), 43-50. DOI=10.5120/18807-0431
Abstract
An intention of MapReduce Sets for Replicated Join expressions analysis has to suggest criteria how Replicated Join expressions in Replicated Join data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for Replicated Join Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific Replicated Join process that are expressiveness of Replicated Join models and reproducibility of their Replicated Join inference. Replicated Join expressions are assumed to be elements of a Replicated Join expression space or Conjecture class and Replicated Join data provide "information" which of these Replicated Join expressions should be used to interpret the Replicated Join data. An inference Replicated Join algorithm constructs the mapping between Replicated Join data and Replicated Join expressions, in particular by a Replicated Join cost minimization process. Fluctuations in the Replicated Join data often limit the Replicated Join precision, which we can achieve to uniquely identify a single Replicated Join expression as interpretation of the Replicated Join data. We advocate an information theoretic perspective on Replicated Join expression analysis to resolve this dilemma where the tradeoff between Replicated Join informativeness of statistical inference Replicated Join and their Replicated Join stability is mirrored in the information-theoretic Replicated Join optimum of high Replicated Join information rate and zero communication expression error. The inference Replicated Join algorithm is considered as an outlier object Replicated Join path, which naturally limits the resolution of the Replicated Join expression space given the uncertainty of the Replicated Join 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 Reduce Side Join Part File Expressions Equal MapReduce Structure into Task Consequences, Performance? International Journal of Computer Applications, Volume 105(2):8-15, November 2014
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