Defining software architectures for big data enabled operator support systems

Klöpper, Benjamin; Dix, Marcel; Schorer, Lukas; Ampofo, Ann; Atzmueller, Martin; Arnu, David; Klinkenberg, Ralf

doi:10.1109/indin.2016.7819366

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The Machine Learning Life Cycle In Chemical Operations1

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Cited by 13 publications

(1 citation statement)

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“…For general manufacturing, 49 describes an engineering process that uses conventional user stories to capture the user needs and intentions in relation to an ML system. Specifically, the chemical industry is addressed in 50 where different types of templates, workshops and interviews are proposed to capture both functional and non‐functional requirements of big data and ML‐based operator support systems.…”

Section: The Machine Learning Life Cycle In Chemical Operationsmentioning

confidence: 99%

The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

Gärtler

Khaydarov

Klöpper

et al. 2021

Chemie Ingenieur Technik

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Artificial intelligence (AI) has received a lot of attention with many publications in recent years. Interestingly related projects in the industry are mostly still in their early stages. We are convinced that progress will only be possible if the entire machine learning (ML) life cycle is considered. Our study focuses on the practical challenges, uses a recent study as foundation and adopts the life-cycle description, highlights the life-cycle practices in other domains and formulates research directions that can help to improve the utilization of AI and machine learning in the process industry.

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Section: The Machine Learning Life Cycle In Chemical Operationsmentioning

confidence: 99%