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Equilibrium and Extreme Principles in Discovering Unknown Relationships from Big Data:Part 1: Methods of Advanced Data Analytics in Light of the Equations of Mathematical Physics

FEATURED PAPER

By Pavel Barseghyan, PhD

Yerevan, Armenia and Plano, Texas, USA
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Abstract

The further development of the methods of Advanced Data Analytics requires using the extreme principles and balance equations for identifying relationships hidden in the data. These fundamental methods and principles are widely used in the classical areas of quantitative science.

Besides, any area of the classical quantitative science, including mechanics and electrodynamics, can be represented as an area of ​​Big Data. For doing that, it is sufficient to collect a large amount of data of electrodynamic or mechanical nature from the surrounding environment.

Having the scientific representations of some quantitative area in the form of fundamental equations on the one side, and in the form of Big Data on the other side, a natural question arises of whether there is a correspondence and an agreement between these two views?

Also, if there is such a correspondence between them, are unambiguous mutual transitions between the fundamental equations and the Big Database possible?

In particular, is it possible to obtain the well-known fundamental equations or some of their equivalents for a quantitative field of knowledge from its Big Data in a statistical or semi statistical way?

This paper discusses the mutual relationships and the possible transitions between the fundamental equations of a quantitative science and their corresponding Big Data bases.

The purpose of the paper is to show that the experience of classical quantitative science can be effectively used in the development of contemporary Big Data Analytics in order to create more reliable methods for detecting unknown patterns and relationships hidden in Big Data.

The second part of this work will be devoted to the applications of the principles and approaches proposed in this paper, for analyzing Big Data in the project management area.

Introduction

Imagine electromagnetic phenomena and processes that taking place in our environment all the time. By collecting the results of measurements of even a tiny part of these processes one can have a typical Big Data base of electromagnetic nature.

But we do not make such measurements because we have a fundamental means of describing these phenomena, such as Maxwell’s equations and their simplified versions in the form of the wave equation, Kirchhoff’s laws, Ohm’s Law, and other laws [1].

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About the Author 

flag-usapavel-barseghyanPavel Barseghyan, PhD     

Yerevan, Armenia

Plano, Texas, USA

Dr. Pavel Barseghyan is a consultant in the field of quantitative project management, project data mining and organizational science. Has over 40 years’ experience in academia, the electronics industry, the EDA industry and Project Management Research and tools development. During the period of 1999-2010 he was the Vice President of Research for Numetrics Management Systems. Prior to joining Numetrics, Dr. Barseghyan worked as an R&D manager at Infinite Technology Corp. in Texas. He was also a founder and the president of an EDA start-up company, DAN Technologies, Ltd. that focused on high-level chip design planning and RTL structural floor planning technologies. Before joining ITC, Dr. Barseghyan was head of the Electronic Design and CAD department at the State Engineering University of Armenia, focusing on development of the Theory of Massively Interconnected Systems and its applications to electronic design. During the period of 1975-1990, he was also a member of the University Educational Policy Commission for Electronic Design and CAD Direction in the Higher Education Ministry of the former USSR. Earlier in his career he was a senior researcher in Yerevan Research and Development Institute of Mathematical Machines (Armenia). He is an author of nine monographs and textbooks and more than 100 scientific articles in the area of quantitative project management, mathematical theory of human work, electronic design and EDA methodologies, and tools development. More than 10 Ph.D. degrees have been awarded under his supervision. Dr. Barseghyan holds an MS in Electrical Engineering (1967) and Ph.D. (1972) and Doctor of Technical Sciences (1990) in Computer Engineering from Yerevan Polytechnic Institute (Armenia).  Pavel’s publications can be found here: http://www.scribd.com/pbarseghyan and here: http://pavelbarseghyan.wordpress.com/.  Pavel can be contacted at [email protected]