Principles of Green Data Mining

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dc.contributor.authorSchneider, Johannes
dc.contributor.authorBasalla, Marcus
dc.contributor.authorSeidel, Stefan
dc.date.accessioned2019-01-03T00:00:07Z
dc.date.available2019-01-03T00:00:07Z
dc.date.issued2019-01-08
dc.description.abstractThis 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.250
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59646
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSustainability in the Fourth Industrial Age: Technologies, Systems and Analytics
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectGreen Computing
dc.subjectData Science
dc.subjectMachine Learning
dc.subjectElectricity Consumption
dc.subjectCRISP-DM
dc.titlePrinciples of Green Data Mining
dc.typeConference Paper
dc.type.dcmiText

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