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

Ravi (Ravinder) Prakash G., Kiran M.. Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Ravi (Ravinder) Prakash G., Kiran M.
10.5120/ijais2016451569
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  1. Ravi (Ravinder) Prakash G. and Kiran M.. How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?. International Journal of Applied Information Systems 11(1):22-39, June 2016. URL, DOI BibTeX

    @article{10.5120/ijais2016451569,
    	author = "Ravi (Ravinder) Prakash G. and Kiran M.",
    	title = "How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "June 2016",
    	volume = 11,
    	number = 1,
    	month = "Jun",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "22-39",
    	numpages = 17,
    	url = "http://www.ijais.org/archives/volume11/number1/903-2016451569",
    	doi = "10.5120/ijais2016451569",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, 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.

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Keywords

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