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Advances in Info-MetricsInformation and Information Processing across Disciplines$
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Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah

Print publication date: 2020

Print ISBN-13: 9780190636685

Published to Oxford Scholarship Online: December 2020

DOI: 10.1093/oso/9780190636685.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 17 June 2021

Cost-Benefit Analysis of Data Intelligence—Its Broader Interpretations

Cost-Benefit Analysis of Data Intelligence—Its Broader Interpretations

Chapter:
(p.433) 16 Cost-Benefit Analysis of Data Intelligence—Its Broader Interpretations
Source:
Advances in Info-Metrics
Author(s):

Min Chen

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190636685.003.0016

The core of data science is our fundamental understanding about data intelligence processes for transforming data to decisions. One aspect of this understanding is how to analyze the cost-benefit of data intelligence workflows. This work is built on the information-theoretic metric proposed by Chen and Golan for this purpose and several recent studies and applications of the metric. We present a set of extended interpretations of the metric by relating the metric to encryption, compression, model development, perception, cognition, languages, and media.

Keywords:   information theory, cost-benefit analysis, data science, data intelligence, workflow optimization, encryption, compression, machine learning, model development, perception, cognition, languages, media

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