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Equilibrium and Extreme Principles in Discovering Unknown Relationships from Big Data Part 2: Non-statistical Mathematical Methods in Project Management

FEATURED PAPER 

By Pavel Barseghyan, PhD 

Yerevan, Armenia and Plano, Texas, USA
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Abstract

The pure statistical methods of Big Data mining are too data dependent, which makes them error prone to a large extent. The semi-statistical methods of Advanced Data Analytics are to some extent better, because they use data grouping/structuring before applying the statistical methods. For this reason the Advanced Data Analytics can be considered as partially data dependent and less error prone.

In order to avoid the disadvantages of pure statistical and semi-statistical methods, non-statistical methods should be used instead.

The goal of non-statistical methodologies in Big Data analysis is to derive functional relationships between the parameters of the system under study in a pure analytical way, and then apply them for data analysis and interpretation.

Non-statistical ways of quantitative description such as state equations or variational principles are common in theoretical physics and other sciences.

The second part of this paper is devoted to demonstrating how the new analytical way of Big Data Analysis can be used in the project management area. In particular it shows how this new non-statistical, analytical methodology can be used to discover unknown relationships between project parameters.

Key words: Big Data Analytics, Non-statistical methods, State equation, Variational principles, Equilibrium of projects, Functional relationships between project parameters.

Introduction

The causal relationships that lie behind the data and facts are typically the logical basis for the processing and interpretation of statistical data collected in specific areas of human activity. These causal relationships have a central role for the expert management of processes that are based on intuition and experience of humans.

If the data is processed by purely statistical methods, without structuring the data, the results are usually highly dependent on the specific data. Sometimes the results can even be almost meaningless because that direct dependency on data.

The likelihood that the patterns and relationships extracted from unstructured data by using pure statistical methods will be qualitatively adequate is negligible. In that case, if these results are qualitatively inadequate and hence they do not correctly reflect the trends contained in the data, than it is meaningless to even talk about their quantitative adequacy.

Qualitative or behavioral adequacy of patterns, extracted from the data can be improved by structuring the data, or splitting data points into groups based on sound principles or local hypotheses.

Increasing the degree of generality and logical validity of these principles and hypotheses improves the probability of retrieving qualitatively and behaviorally adequate relationships from data.

The gradual improvement of understanding and interpretation of causal relationships contained in the data, and the transition from the level of using intuition, experience and statistical perception of information to the level of systematization of knowledge in the form of mathematical models and quantitative theories, makes it possible to give a completely new, non-statistical interpretation of data.

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About the Author 

flag-Armenia-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]