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How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?

by Ravi (Ravinder) Prakash G., Kiran M.
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 1
Year of Publication: 2016
Authors: Ravi (Ravinder) Prakash G., Kiran M.
10.5120/ijais2016451569

Ravi (Ravinder) Prakash G., Kiran M. . How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?. International Journal of Applied Information Systems. 11, 1 ( Jun 2016), 22-39. DOI=10.5120/ijais2016451569

@article{ 10.5120/ijais2016451569,
author = { Ravi (Ravinder) Prakash G., Kiran M. },
title = { How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel? },
journal = { International Journal of Applied Information Systems },
issue_date = { Jun 2016 },
volume = { 11 },
number = { 1 },
month = { Jun },
year = { 2016 },
issn = { 2249-0868 },
pages = { 22-39 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number1/903-2016451569/ },
doi = { 10.5120/ijais2016451569 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:03:41.910864+05:30
%A Ravi (Ravinder) Prakash G.
%A Kiran M.
%T How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 1
%P 22-39
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We develop a novel technique for resizable Hadoop cluster’s lower bounds, the template matching rectangular array of inverted Index summarization expressions. Specifically, fix an arbitrary hybrid kernel function f:{0,1}n → {0,1} and let Af be the rectangular array of inverted Index summarization expressions whose columns are each an application of f to some subset of the variables x1,x2,…,x4n ). We prove that Af has bounded-capacity resizable Hadoop cluster’s complexity Ω(d), where d is the approximate degree of f. This finding remains valid in the MapReduce programming model, regardless of prior measurement. In particular, it gives a new and simple proof of lower bounds for robustness and other symmetric conjunctive predicates. We further characterize the discrepancy, approximate PageRank, and approximate trace distance norm of Af in terms of well-studied analytic properties of f, broadly generalizing several findings on small-bias resizable Hadoop cluster and agnostic inference. The method of this paper has also enabled important progress in multi-cloud resizable Hadoop cluster’s complexity.

References
  1. Ravi Prakash G, Kiran M and Saikat Mukherjee. 2014. On Randomized Preference Limitation Protocol for Quantifiable Shuffle and Sort Behavioral Implications in MapReduce Programming Model. Parallel & Cloud Computing 3, Issue 1, 1-14.
  2. Greenlaw, R. and Kantabutra. 2008. On the parallel complexity of hierarchical clustering and CC-complete problems. Complexity 14, 18-28. (doi:10.1002/cplx.20238)
  3. Ravi (Ravinder) Prakash G, Kiran M. 2014. On The Least Economical MapReduce Sets for Summarization Expressions. International Journal of Computer Applications 94, 13-20. (doi: 10.5120/16354-5732)
  4. Amazon Elastic MapReduce. http://aws.amazon.com/elasticmapreduce/
  5. Gerasimos G. Potamianos, John K. Goutsias. 1997. Stochastic approximation algorithms for partition function estimation of Gibbs random fields. IEEE Transactions on Information Theory 43, 1948-1965. (doi: 10.1109/18.641558)
  6. N. Ailon, B. Chazelle, S. Comandur, D. Liu. 2007. Estimating the Distance to a Monotone Function. Random Structures and Algorithms 31, 371-383. (doi:10.1002/rsa.20167)
  7. A. Gavish, Abraham Lempel. 1996. Match-length functions for data compression. IEEE Transactions on Information Theory 42, 1375-1380. (doi:10.1109/18.532879)
  8. Jonathan J. Ashley. 1988. A linear bound for sliding-block decoder window size. IEEE Transactions on Information Theory 34, 389-399. (doi:10.1109/18.6020)
  9. Ping Wah Wong. 1997. Rate distortion efficiency of subband coding with crossband prediction. IEEE Transactions on Information Theory 43, 352-356. (doi:10.1109/18.567761)
  10. A. Lafourcade, Alexander Vardy. 1996. Optimal sectionalization of a trellis. IEEE Transactions on Information Theory 42, 689-703. (doi: 10.1109/18.490504)
  11. T.M. Cover. 1998. Comments on Broadcast Channels. IEEE Transactions on Information Theory 44, 2524-2530. (doi: 10.1109/18.720547)
  12. A. Lapidoth and P. Narayan. 1998. Reliable Communication Under Channel Uncertainty. IEEE Transactions on Information Theory 44, 2148-2177. (doi:10.1109/18.720535)
  13. Ralph Lorentzen, Raymond Nilsen. 1991. Application of linear programming to the optimal difference triangle set problem (Corresp.). IEEE Transactions on Information Theory 37, 1486-1488. (doi:10.1109/18.133274)
  14. Alfred J. Menezes, Tatsuaki Okamoto, Scott A. Vanstone. 1993. Reducing elliptic curve logarithms to logarithms in a finite field. IEEE Transactions on Information Theory 39, 1639-1646. (doi:10.1109/18.259647)
  15. Leo Breiman. 1993. Hinging hyperplanes for regression, classification, and function approximation. IEEE Transactions on Information Theory 39, 999-1013. (doi:10.1109/18.256506)
  16. S. R. Kulkarni, D. N.C. Tse. 1994. A paradigm for class identification problems. IEEE Transactions on Information Theory 40, 696-705. (doi:10.1109/18.335881)
  17. Donald Miner, Adam Shook, 2013, "MapReduce Design Patterns" O’Reilly Media, Inc.: 978-1-449-32717-0.
  18. Rudolf F. Ahlswede, Zhen Zhang. 1994. On multiuser write-efficient memories. IEEE Transactions on Information Theory 40, 674-686. (doi:10.1109/18.335880)
  19. B. Chazelle. 2000. The Discrepancy Method: Randomness and Complexity. Cambridge University Press. 978-0-521-77093-9.
  20. B. Chazelle, A. Lvov. 2001. A Trace Bound for the Hereditary Discrepancy. Discrete Computational. Geom. 26, 221-231. (doi:10.1007/s00454-001-0030-2)
  21. B. Chazelle, A. Lvov. 2001. The Discrepancy of Boxes in Higher Dimension. Discrete Computational. Geom. 25, 519-524. (doi:10.1007/s00454-001-0014-2)
  22. B. Chazelle, J. Matoušek, M. Sharir. 1995. An Elementary Approach to Lower Bounds in Geometric Discrepancy. Discrete Comput. Geom. 13, 363-381. (doi:10.1007/BF02574050)
  23. E. Arikan. 1994. An upper bound on the zero-error list-coding capacity. IEEE Transactions on Information Theory 40, 1237-1240. (doi:10.1109/18.335947)
  24. B. Chazelle, H. Edelsbrunner, L.J. Guibas, M. Sharir. 1991. A Singly Exponential Stratification Scheme for Real Semi-Algebraic Varieties and Its Applications. Theoretical Computer Science 84, 77-105. (doi:10.1016/0304-3975(91)90261-Y)
  25. Neri Merhav. 1989. The estimation of the model order in exponential families (Corresp.). IEEE Transactions on Information Theory 35, 1109-1114. (doi:10.1109/18.42231)
  26. B. Chazelle. 1999. Discrepancy Bounds for Geometric Set Systems with Square Incidence Matrices. Advances in Discrete and Computational Geometry, Contemporary Mathematics AMS 223, 103-107.
  27. B. Chazelle. 2004. The Discrepancy Method in Computational Geometry. Handbook of Discrete and Computational Geometry, CRC Press 44, 983-996.
  28. Fadika, Z.; Govindaraju, M. 2010. LEMO-MR: Low Overhead and Elastic MapReduce Implementation Optimized for Memory and CPU-Intensive Applications. IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 1-8. (doi:10.1109/CloudCom.2010.45)
  29. Fadika, Z.; Govindaraju, M. 2011. DELMA: Dynamically Elastic MapReduce Framework for CPU-Intensive Applications. 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 454-463. (doi: 10.1109/CCGrid.2011.71)
  30. Iordache, A.; Morin, C.; Parlavantzas, N.; Feller, E.; Riteau, P. 2013. Resilin: Elastic MapReduce over Multiple Clouds. 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 261-268. (doi:10.1109/CCGrid.2013.48)
  31. XiaoyongXu; Maolin Tang. 2013. A comparative study of the semi-elastic and fully-elastic mapreduce models. IEEE International Conference on Granular Computing (GrC), 380-385. (doi:10.1109/GrC.2013.6740440)
  32. Wei Xiang Goh; Kian-Lee Tan. 2014. Elastic MapReduce Execution. 14th IEEE/ACM, International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 216-225. (doi:10.1109/CCGrid.2014.14)
  33. B. Chazelle, W. Mulzer. 2011. Computing Hereditary Convex Structures. Discrete Comput. Geom. 45, 796-823. (doi:10.1007/s00454-011-9346-8)
  34. B. Chazelle, H. Edelsbrunner, M. Grigni, L.J. Guibas, M. Sharir, E. Welzl. 1995. Improved Bounds on Weak e-Nets for Convex Sets. Discrete Comput. Geom. 13, 1-15. (doi:10.1007/BF02574025)
  35. David P. Williamson, David B. Shmoys. 2011. The Design of Approximation Algorithms.Cambridge University Press, 978-0-521-19527-0.
  36. Oded Goldreich. 2008. Computational Complexity: A Conceptual Perspective.Cambridge University Press, 978-0-521-88473-0.
  37. Sanjeev Arora, Boaz Barak. 2009. Computational Complexity: A Modern Approach.Cambridge University Press, 978-0-521-42426-4.
  38. Dimitri P. Bertsekas, Convex Optimization Algorithms, Athena Scientific, Hardcover Edition ISBN: 1-886529-28-0, 978-1-886529-28-1, Publication: February, 2015, 576 pages.
  39. Ravi (Ravinder) Prakash G, Kiran M. "Does there exist lower bounds on numerical summarization for calculating aggregate resizable Hadoop channel and complexity?" International Journal of Advanced Information Science and Technology, April 2016, Pages: 26-44, ISSN: 2319:2682
Index Terms

Computer Science
Information Sciences

Keywords

Inverted Index summarization Bounded-Capacity Resizable Hadoop Cluster Complexity Discrepancy Trace Distance Norm and Finite string Representation

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