ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data
Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the poten...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8876834/ |
| _version_ | 1818415876477550592 |
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| author | Jose M. Alves Leonardo M. Honorio Miriam A. M. Capretz |
| author_facet | Jose M. Alves Leonardo M. Honorio Miriam A. M. Capretz |
| author_sort | Jose M. Alves |
| collection | DOAJ |
| description | Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may not have a final desirable outcome. This paper proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows, particularly on large volumes of data series. The ML4IoT framework enables the implementation of several types of ML models, each one with a different workflow. These models can be easily configured and used through a simple pipeline. ML4IoT has been designed to use container-based components to enable training and deployment of various ML models in parallel. The results obtained suggest that the proposed framework can manage real-world IoT heterogeneous data by providing elasticity, robustness, and performance. |
| first_indexed | 2024-12-14T11:41:57Z |
| format | Article |
| id | doaj.art-3bc030293e564b2d95b669863e14b7b7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:41:57Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3bc030293e564b2d95b669863e14b7b72022-12-21T23:02:46ZengIEEEIEEE Access2169-35362019-01-01715295315296710.1109/ACCESS.2019.29481608876834ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things DataJose M. Alves0Leonardo M. Honorio1https://orcid.org/0000-0003-2735-4792Miriam A. M. Capretz2https://orcid.org/0000-0002-1380-971XDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaDepartment of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora-MG, BrazilDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaInternet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may not have a final desirable outcome. This paper proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows, particularly on large volumes of data series. The ML4IoT framework enables the implementation of several types of ML models, each one with a different workflow. These models can be easily configured and used through a simple pipeline. ML4IoT has been designed to use container-based components to enable training and deployment of various ML models in parallel. The results obtained suggest that the proposed framework can manage real-world IoT heterogeneous data by providing elasticity, robustness, and performance.https://ieeexplore.ieee.org/document/8876834/Big datacontainer-based virtualizationIoTmachine learningmachine learning workflowmicroservices |
| spellingShingle | Jose M. Alves Leonardo M. Honorio Miriam A. M. Capretz ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data IEEE Access Big data container-based virtualization IoT machine learning machine learning workflow microservices |
| title | ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data |
| title_full | ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data |
| title_fullStr | ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data |
| title_full_unstemmed | ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data |
| title_short | ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data |
| title_sort | ml4iot a framework to orchestrate machine learning workflows on internet of things data |
| topic | Big data container-based virtualization IoT machine learning machine learning workflow microservices |
| url | https://ieeexplore.ieee.org/document/8876834/ |
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