• DocumentCode
    1791621
  • Title

    Big Automotive Data: Leveraging large volumes of data for knowledge-driven product development

  • Author

    Johanson, Mathias ; Belenki, Stanislav ; Jalminger, Jonas ; Fant, Magnus ; Gjertz, Mats

  • Author_Institution
    Alkit Commun. AB, Mölndal, Sweden
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    736
  • Lastpage
    741
  • Abstract
    To be successful in the increasingly competitive consumer vehicle market, automotive manufacturers must be highly responsive to customer needs and market trends, while responding to the challenges of climate change and sustainable development. One key to achieving this is to promote knowledge-driven product development through large scale collection of data from connected vehicles, to capture customer needs and to gather performance data, diagnostic data and statistics. Since the volume of data collected from fleets of vehicles using telematics services can be very high, it is important to design the systems and frameworks in a way that is highly scalable and efficient. This can be described as a Big Data challenge in an automotive context. In this paper, we explore the opportunities of leveraging Big Automotive Data for knowledge driven product development, and we present a technological framework for capture and online analysis of data from connected vehicles.
  • Keywords
    Big Data; automobile manufacture; automotive electronics; customer services; data analysis; product development; Big Data challenge; automotive manufacturer; big automotive data; consumer vehicle market; customer needs; knowledge-driven product development; market trend; online data analysis; telematics services; Automotive engineering; Big data; Context; Monitoring; Product development; Telematics; Vehicles; analytics; automotive telematics; big data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004298
  • Filename
    7004298