Abstract
Following the Industry 4.0 paradigm, many industrial companies capture high volume of time series data from their industrial processes. A common task to generate value from this data is to visualize and analyze it. However, regular visualization approaches of this data require specialized hardware. Thus, downsampling algorithms as M4 are required to create a simplified view of the original data, which requires less computation power to be visualized while keeping as much information as possible from 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. Moreover, most of these algorithms require regularly sampled intervals, while data from many industrial processes does not fulfil this condition. This paper addresses these issues. The paper proposes an adaptive extension of the M4 algorithm suitable for multivariate datasets. The algorithm has been successfully tested with synthetic multivariate time series and commodity hardware, validating its suitability for the visualization and analysis of time series from industrial processes.
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Acknowledgement
This research was partially funded by the Elkartek program of the department of industry of the Basque Government (KK-2019/00022).
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Franco, J., Garcia, A., Gil, A. (2022). Multivariate Adaptive Downsampling Algorithm for Industry 4.0 Data Visualization. In: Sanjurjo González, H., Pastor López, I., GarcÃa Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_56
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DOI: https://doi.org/10.1007/978-3-030-87869-6_56
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