Principles of Green Data Mining
Files
Date
2019-01-08
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
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.
Description
Keywords
Sustainability in the Fourth Industrial Age: Technologies, Systems and Analytics, Decision Analytics, Mobile Services, and Service Science, Green Computing, Data Science, Machine Learning, Electricity Consumption, CRISP-DM
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 52nd Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.