Cost-Benefit Analysis of Data Intelligence -- Its Broader Interpretations
Other Computer Science
2018-12-04 v2
Abstract
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 news media.
Cite
@article{arxiv.1805.08575,
title = {Cost-Benefit Analysis of Data Intelligence -- Its Broader Interpretations},
author = {Min Chen},
journal= {arXiv preprint arXiv:1805.08575},
year = {2018}
}
Comments
The first version was archived in May 2018. It was updated in December 2018 following a minor revision according to the reviewers' comments and suggestions