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Data Requirements for Robust Machine Learning in High Variance Industrial Environments

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dc.contributor.authorGhanem, Abraham
dc.contributor.editorHerrmann, Andrea
dc.date.accessioned2023-11-30T08:32:47Z
dc.date.available2023-11-30T08:32:47Z
dc.date.issued2023
dc.description.abstractMachine Learning (ML) based industrial applications deployed in high variance dynamic environments present a new set of challenges. The performance of such systems is directly linked to the nature of the data it has been subjected to. Using the computer vision-based ML applications in the logistics industry as a case study, due to their high variance environment and strict requirements, we try to address the issue of understanding the data requirements for the successful development and deployment of such applications. We propose a systematic approach to address high variance scenarios with limited relevant data availability, covering both real data collection and synthetic data generation, highlighting their requirements and effective utilization methods.en
dc.identifier.issn0720-8928
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43240
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 43, Heft 4
dc.relation.ispartofseriesSoftwaretechnik-Trends
dc.subjectMachine Learning
dc.subjectlogistics industry
dc.subjectcase study
dc.subjectdata requirements
dc.titleData Requirements for Robust Machine Learning in High Variance Industrial Environmentsen
dc.typeText/Conference Paper
mci.conference.date10-11 August 2023
mci.conference.locationDortmund
mci.conference.sessiontitleFachgruppentreffen Requirements Engineering 2023
mci.reference.pages56-57

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Softwaretechnik-Trends 43(4) - 2023