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Big data modelling the knowledge economy

Big data modelling the knowledge economy A computer-generated 3D model illustrates the advantages of virtual in silico techniques. Derived from data for SMEs in service industries it enables a business owner (or consultant) to identify where any organisation is on a three-dimensional landscape and draw quantitative conclusions about fruitful future directions of travel plus how high the resulting benefits will be and what costs are due along the journey. This 'ready-to-go' landscape map is of immense value for academics and practitioners alike, and is easily-applicable. Anyone can create the three-dimensional fold and discuss the implications of growth and development with specific clients. Markov Chain Monte Carlo modelling is presented which, put simply, is throwing virtual balls down the basic fold to show how to predict outcomes of Knowledge Engineering projects. Results are shown for; adding multiskilled innovators, adding network input from external environments, costing management control effectively and explaining how IPR adds extraneous value. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge-Based Development Inderscience Publishers

Big data modelling the knowledge economy

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
2040-4468
eISSN
2040-4476
DOI
10.1504/IJKBD.2018.094896
Publisher site
See Article on Publisher Site

Abstract

A computer-generated 3D model illustrates the advantages of virtual in silico techniques. Derived from data for SMEs in service industries it enables a business owner (or consultant) to identify where any organisation is on a three-dimensional landscape and draw quantitative conclusions about fruitful future directions of travel plus how high the resulting benefits will be and what costs are due along the journey. This 'ready-to-go' landscape map is of immense value for academics and practitioners alike, and is easily-applicable. Anyone can create the three-dimensional fold and discuss the implications of growth and development with specific clients. Markov Chain Monte Carlo modelling is presented which, put simply, is throwing virtual balls down the basic fold to show how to predict outcomes of Knowledge Engineering projects. Results are shown for; adding multiskilled innovators, adding network input from external environments, costing management control effectively and explaining how IPR adds extraneous value.

Journal

International Journal of Knowledge-Based DevelopmentInderscience Publishers

Published: Jan 1, 2018

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