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  • 标题:INDUSTRY 4.0 REVOLUTION IN AUTONOMOUS AND CONNECTED VEHICLE A NON-CONVENTIONAL APPROACH TO MANAGE BIG DATA
  • 本地全文:下载
  • 作者:ALESSANDRA PIERONI ; NOEMI SCARPATO ; MARCO BRILLI
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2018
  • 卷号:96
  • 期号:1
  • 页码:10
  • 出版社:Journal of Theoretical and Applied
  • 摘要:In literature, the term Industry 4.0 is used to represent the current Industrial Revolution. This revolution, however, involves different sectors: from manufacturing to healthcare. Indeed, the current revolution is also known as the Internet of Things or Internet of Everything or the Industrial Internet, and it is a vision rather than a technological improvement. Industry 4.0 is able to create value during the entire product life cycle. For the reasons described above, the outcome of this revolution may be an object but also a service designed for a final user, whose development is driven by innovation in several areas: IT, embedded systems, production, automation technique and mechanical engineering, all of them combined in order to deliver more complex systems as we know today. In this scenario, Autonomous and Connected Vehicles (ACV) represent the perfect connection between digital and real world, an issue that stands in the center of Industry 4.0. This article intends to present a brief overview of the Autonomous and Connected Vehicles, while proposing a non-conventional approach to manage the great amount of data generated by the sensors disseminated inside the vehicle and all around itself, in other words, in the urban environment where the vehicle is completely spread-in. In this paper, we address the issue of the calculation of the paths of vehicles totally connected. We present two approaches: the relational approach and the GraphDB solution. Our intent is to demonstrate that the GraphDB approach is more convenient and easy respect to RDBMS solution especially in relation to real-time big-data management.
  • 关键词:Autonomous Vehicle; Connected Vehicle; Big Data; GraphDB; Internet of Things; Industry 4.0
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