An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics

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 wi...

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Autores principales: Salierno, Giulio, Morvillo, Sabatino, Leonardi, Letizia, Cabri, Giacomo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225517/
http://dx.doi.org/10.1007/978-3-030-49165-9_3
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author Salierno, Giulio
Morvillo, Sabatino
Leonardi, Letizia
Cabri, Giacomo
author_facet Salierno, Giulio
Morvillo, Sabatino
Leonardi, Letizia
Cabri, Giacomo
author_sort Salierno, Giulio
collection PubMed
description 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.
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spelling pubmed-72255172020-05-15 An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics Salierno, Giulio Morvillo, Sabatino Leonardi, Letizia Cabri, Giacomo Advanced Information Systems Engineering Workshops Article 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. 2020-04-29 /pmc/articles/PMC7225517/ http://dx.doi.org/10.1007/978-3-030-49165-9_3 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Salierno, Giulio
Morvillo, Sabatino
Leonardi, Letizia
Cabri, Giacomo
An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
title An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
title_full An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
title_fullStr An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
title_full_unstemmed An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
title_short An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics
title_sort architecture for predictive maintenance of railway points based on big data analytics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225517/
http://dx.doi.org/10.1007/978-3-030-49165-9_3
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