Multivariate Adaptive Downsampling Algorithm for Industry 4.0 Visual Analytics
Autores: Franco, Javier
Fecha: 01.01.2024
Cybernetics and Systems
Abstract
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.
BIB_text
author = {Franco, Javier},
title = {Multivariate Adaptive Downsampling Algorithm for Industry 4.0 Visual Analytics},
journal = {Cybernetics and Systems},
pages = {17},
volume = {Vol. 55},
keywds = {
multivariate time series; visual analytics
}
abstract = {
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.
}
doi = {10.1080/01969722.2023.2240650},
date = {2024-01-01},
}
