HollerithEnergyML: A Prototype of a Machine Learning Energy Consumption Recommender System
| dc.contributor.author | Zanger, Michael | |
| dc.contributor.author | Schulz, Alexander | |
| dc.contributor.author | Grodmeier, Lukas | |
| dc.contributor.author | Agaj, Dion | |
| dc.contributor.author | Schindler, Rafael | |
| dc.contributor.author | Weiss, Lukas | |
| dc.contributor.author | Möhring, Michael | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Gergeleit, Martin | |
| dc.contributor.editor | Martin, Ludger | |
| dc.date.accessioned | 2024-10-21T18:24:14Z | |
| dc.date.available | 2024-10-21T18:24:14Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Energy 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.doi | 10.18420/inf2024_132 | |
| dc.identifier.isbn | 978-3-88579-746-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45106 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2024 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-352 | |
| dc.subject | AI | |
| dc.subject | Energy Consumption | |
| dc.subject | ML | |
| dc.subject | Recommender | |
| dc.title | HollerithEnergyML: A Prototype of a Machine Learning Energy Consumption Recommender System | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 1523 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 1519 | |
| gi.conference.date | 24.-26. September 2024 | |
| gi.conference.location | Wiesbaden | |
| gi.conference.sessiontitle | Data Analytics as a Service - Challenges and Opportunities (DAS2024) |
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