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HollerithEnergyML: A Prototype of a Machine Learning Energy Consumption Recommender System

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dc.contributor.authorZanger, Michael
dc.contributor.authorSchulz, Alexander
dc.contributor.authorGrodmeier, Lukas
dc.contributor.authorAgaj, Dion
dc.contributor.authorSchindler, Rafael
dc.contributor.authorWeiss, Lukas
dc.contributor.authorMöhring, Michael
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:14Z
dc.date.available2024-10-21T18:24:14Z
dc.date.issued2024
dc.description.abstractEnergy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.en
dc.identifier.doi10.18420/inf2024_132
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45106
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectAI
dc.subjectEnergy Consumption
dc.subjectML
dc.subjectRecommender
dc.titleHollerithEnergyML: A Prototype of a Machine Learning Energy Consumption Recommender Systemen
dc.typeText/Conference Paper
gi.citation.endPage1523
gi.citation.publisherPlaceBonn
gi.citation.startPage1519
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleData Analytics as a Service - Challenges and Opportunities (DAS2024)

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