Multivariate Adaptive Downsampling Algorithm for Industry 4.0 Visual Analytics

Many industrial companies capture high volume of time series data from their industrial processes. However, to visualize it, regular visualization approaches require specialized hardware. Thus, downsampling algorithms are required to create a simplified view of the original data. Although industrial...

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Bibliographic Details
Published in:Cybernetics and systems 2024-08, Vol.55 (6), p.1399-1416
Main Authors: Franco, Javier, Garcia, Ander, Gil, Amaia, Ferrando, Juan Luis, Badiola, Xabier, Saez de Buruaga, Mikel
Format: Article
Language:English
Subjects:
Downsampling
multivariate time series
visual analytics
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Summary:Many industrial companies capture high volume of time series data from their industrial processes. However, to visualize it, regular visualization approaches require specialized hardware. Thus, downsampling algorithms are required to create a simplified view of the original data. Although industrial processes involve synchronized variables that should be visualized together for their analysis, existing downsampling algorithms tackle visualization of univariate data series. This paper proposes an adaptive extension of the M4 algorithm for multivariate datasets. The algorithm has been validated successfully with data from a conventional 3D turning operation and commodity hardware. For validation, a direct image comparison has been performed.
ISSN:0196-9722
1087-6553
DOI:10.1080/01969722.2023.2240650