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Principles of Green Data Mining

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Title:Principles of Green Data Mining
Authors:Schneider, Johannes
Basalla, Marcus
Seidel, Stefan
Keywords:Sustainability in the Fourth Industrial Age: Technologies, Systems and Analytics
Decision Analytics, Mobile Services, and Service Science
Green Computing
Data Science
Machine Learning
show 2 moreElectricity Consumption
CRISP-DM
show less
Date Issued:08 Jan 2019
Abstract:This paper develops a set of principles for green
data mining, related to the key stages of business un-
derstanding, data understanding, data preparation,
modeling, evaluation, and deployment. The principles
are grounded in a review of the Cross Industry Stand-
ard Process for Data mining (CRISP-DM) model and
relevant literature on data mining methods and Green
IT. We describe how data scientists can contribute to
designing environmentally friendly data mining pro-
cesses, for instance, by using green energy, choosing
between make-or-buy, exploiting approaches to data
reduction based on business understanding or pure
statistics, or choosing energy friendly models.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59646
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.250
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Sustainability in the Fourth Industrial Age: Technologies, Systems and Analytics


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