Massive amounts of data produced by railway systems are a valuable resource to enable Big Data analytics. Despite its richness, several challenges arise when dealing with the deployment of a big data architecture into a railway system. In this paper, we propose a four-layers big data architecture with the goal of establishing a data management policy to manage massive amounts of data produced by railway switch points and perform analytical tasks efficiently. An implementation of the architecture is given along with the realization of a Long Short-Term Memory prediction model for detecting failures on the Italian Railway Line of Milano - Monza - Chiasso.

An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics / Salierno, G.; Morvillo, S.; Leonardi, L.; Cabri, G.. - 382:(2020), pp. 29-40. ((Intervento presentato al convegno 2nd International Workshop on Key Enabling Technologies for Digital Factories, KET4DF 2020 and the 1st International Workshop on Information Systems Engineering for Smarter Life, ISESL 2020, associated with the 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020 tenutosi a Grenoble, France nel 2020 [10.1007/978-3-030-49165-9_3].

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