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A Big Data Architecture for Automotive Applications: PSA Group Deployment Experience

By Amir Haroun, Ahmed Mostefaoui and François Dessables

Abstract

International audienceVehicles have become moving sensor platforms collecting huge volumes of data from their various embedded sensors. This data has a great value for automotive manufacturers and vehicles owners. Indeed, connected vehicles data can be used in a large broad of automotive services ranging from safety services to well-being services (e.g. fatigue detection). However, vehicle fleets send big volumes of data that traditional computing and storage approaches are not able to manage efficiently. In this paper, we present the experience of the PSA Group on leveraging big data in automotive context. We describe in depth the big data architecture deployed within the PSA Group and the underlying technologies/products used in each component

Topics: [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]
Publisher: HAL CCSD
Year: 2017
OAI identifier: oai:HAL:hal-02952696v1
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