Electrical and Computer Engineering Publications
Document Type
Article
Publication Date
10-18-2019
Volume
7
Issue
1
Journal
IEEE Access
First Page
152953
URL with Digital Object Identifier
10.1109/ACCESS.2019.2948160
Last Page
152967
Abstract
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.
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Notes
Publisher version is accessible at https://ieeexplore.ieee.org/document/8876834
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