| dc.contributor.author | Sen, Sagar | |
| dc.contributor.author | Husom, Erik Johannes | |
| dc.contributor.author | Goknil, Arda | |
| dc.contributor.author | Tverdal, Simeon | |
| dc.contributor.author | Nguyen, Phu Hong | |
| dc.contributor.author | Mancisidor, Iker | |
| dc.date.accessioned | 2023-02-28T15:26:54Z | |
| dc.date.available | 2023-02-28T15:26:54Z | |
| dc.date.created | 2022-12-23T14:14:56Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | IEEE Software. 2022, 39 (6), 35-42. | en_US |
| dc.identifier.issn | 0740-7459 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3054778 | |
| dc.description.abstract | We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We present the design and deployment of our approach from an AI engineering perspective using two industrial case studies. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.title | Taming Data Quality in AI-Enabled Industrial Internet of Things | en_US |
| dc.title.alternative | Taming Data Quality in AI-Enabled Industrial Internet of Things | en_US |
| dc.type | Peer reviewed | en_US |
| dc.type | Journal article | en_US |
| dc.description.version | acceptedVersion | en_US |
| dc.source.pagenumber | 35-42 | en_US |
| dc.source.volume | 39 | en_US |
| dc.source.journal | IEEE Software | en_US |
| dc.source.issue | 6 | en_US |
| dc.identifier.doi | 10.1109/MS.2022.3193975 | |
| dc.identifier.cristin | 2097248 | |
| dc.relation.project | EC/H2020/958357 | en_US |
| dc.relation.project | EC/H2020/958363 | en_US |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
| cristin.qualitycode | 2 | |
