Abstract
Nowadays, humans depend on the operation of different complex systems. For example, transportation systems (trains, ships and planes), public services (water, gas and electricity), manufacturing plants, hospitals and banks, to name a few. In recent years, given the improvement of sensors it is normal to have a large amount of data of a complex system. This creates a major challenge for analysis, visualization, and decision making about this data. In these days, it is not enough a statistical analysis of the data, but a machine learning needs to be applied for better inferential information. In this paper, as part of our formation in masters in computer science, we propose a new architecture for a web platform for the management and inference of information based on Machine Learning. It can receive, clean, pre-process and transform the data for the statistical analysis and application of machine learning algorithms. The proposed architecture is validated with experimental tests obtained from a simulator of a hydrogen production biorefinery, but can be abstracted and applied in different complex systems.
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Rodríguez, L.A., Vadillo, C.J., Gómez, J.R., Torres, I. (2019). Education + Industry 4.0: Developing a Web Platform for the Management and Inference of Information Based on Machine Learning for a Hydrogen Production Biorefinery. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_52
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