A new upper bound for Shannon entropy. A novel approach in modeling of Big Data applications

Summary Analyzing data type produced, stored, and aggregated in Big Data environments is a challenge in understanding data quality and represents a crucial support for decisionmaking. Big Data application modeling requires meta‐data modeling, interaction modeling, and execution modeling. Entropy, re...

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Bibliographic Details
Published in:Concurrency and computation 2016-02, Vol.28 (2), p.351-359
Main Authors: Popescu, Pantelimon George, Sluşanschi, Emil-Ioan, Iancu, Voichiţa, Pop, Florin
Format: Article
Language:English
Subjects:
Big Data applications
bound
Concurrency
Data management
Entropy
Entropy (Information)
Inequalities
Jensen's inequality
Mathematical models
Modelling
refinements
Shannon entropy
Upper bounds
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Summary:Summary Analyzing data type produced, stored, and aggregated in Big Data environments is a challenge in understanding data quality and represents a crucial support for decisionmaking. Big Data application modeling requires meta‐data modeling, interaction modeling, and execution modeling. Entropy, relative entropy, and mutual information play important roles in information theory. Our purpose within this paper is to present a new upper bound for the classical Shannon's entropy. The new bound is derived from a refinement of a recent result from the literature, the inequality of S. S. Dragomir (2010). The reasoning is based on splitting the considered interval into the mentioned inequality. The upper bound can be considered in understanding the potential information that each data type may have in a Big Data environment. Copyright © 2014 John Wiley & Sons, Ltd.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.3444