Schneider, JohannesBasalla, MarcusSeidel, Stefan2019-01-032019-01-032019-01-08978-0-9981331-2-6http://hdl.handle.net/10125/59646This 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.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalSustainability in the Fourth Industrial Age: Technologies, Systems and AnalyticsDecision Analytics, Mobile Services, and Service ScienceGreen ComputingData ScienceMachine LearningElectricity ConsumptionCRISP-DMConference Paper10.24251/HICSS.2019.250