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Energy Efficiency in Data Stream Mining
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2015 (English)In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015, 1125-1132 p.Conference paper (Refereed)
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Text
Abstract [en]

Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We extended the CRISP (Cross Industry Standard Process for Data Mining) framework to include energy consumption analysis. Based on this framework, we conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. The results indicate that energy consumption can be reduced by up to 92.5% (557 J) while maintaining accuracy.

Place, publisher, year, edition, pages
2015. 1125-1132 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-11412DOI: 10.1145/2808797.2808863ISI: 000371793500173ISBN: 978-1-4503-3854-7 (print)OAI: oai:DiVA.org:bth-11412DiVA: diva2:894248
Conference
Int’l Symp. on Foundations and Applications of Big Data Analytics (FAB 2015), Paris
Projects
BigData@BTH - Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation
Available from: 2016-01-14 Created: 2016-01-14 Last updated: 2016-09-09Bibliographically approved

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Publisher's full texthttp://doi.acm.org/10.1145/2808797.2808863

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García Martín, EvaLavesson, NiklasGrahn, Håkan
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