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

被引：7

作者：

Haroun, Amir ^{[1
]}

Mostefaoui, Ahmed ^{[2
]}

Dessables, Francois ^{[3
]}

机构：

[1] PSA Grp, RDD, Bessoncourt, France

[2] Bourgognes Franche Comte Univ, CNRS, FEMTO ST Inst, Montbeliard, France

[3] PSA Grp, DDCE, Bessoncourt, France

来源：

2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID) | 2017年

关键词：

Big Data; Reference Architecture; Connected Vehicles; MAPREDUCE;

D O I：

10.1109/CCGRID.2017.107

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Vehicles 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(1) on leveraging big data in automotive context. We describe in depth the big data architecture deployed within the PSA Group and the underlaying technologies/products used in each component.

引用

页码：921 / 928

页数：8

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