Principles of Green Data Mining

Date
2019-01-08
Authors
Schneider, Johannes
Basalla, Marcus
Seidel, Stefan
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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.
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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
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10 pages
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Proceedings of the 52nd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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