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Energy Efficiency in Data Stream Mining
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
2015 (engelsk)Inngår i: ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining / [ed] Jian Pei,Fabrizio Silvestri & Jie Tang, ACM Digital Library, 2015, s. 1125-1132Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
ACM Digital Library, 2015. s. 1125-1132
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Identifikatorer
URN: urn:nbn:se:hj:diva-37925DOI: 10.1145/2808797.2808863ISI: 000371793500173ISBN: 978-1-4503-3854-7 (tryckt)OAI: oai:DiVA.org:hj-37925DiVA, id: diva2:1160021
Konferanse
Int’l Symp. on Foundations and Applications of Big Data Analytics (FAB 2015), Paris
Prosjekter
BigData@BTH - Scalable resource-efficient systems for big data analytics
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2016-01-14 Laget: 2017-11-24 Sist oppdatert: 2018-09-11bibliografisk kontrollert

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