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Data Analytics and Machine Learning for Smart Decision Making in Automotive Sector

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Enterprise Design, Operations, and Computing. EDOC 2022 Workshops (EDOC 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 466))

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Abstract

The objective of this thesis is to conduct scientific research on the use of data science and artificial intelligence techniques in the practices of automotive dealership companies to assist them in their decision-making processes and to use data-driven methods with modeling approaches for computing these enterprises. By proposing algorithms capable of continuously extracting relevant information from a diverse and multi-structured automotive environment. Due to the large amount of data available within these companies, we will develop algorithms to correctly assess the situation, suggest recommendations for decision-making, develop marketing strategies, and automate manual tasks that cost time, effort, and money.

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Author information

Authors and Affiliations

  1. LIB Laboratory, University of Burgundy, Dijon, France

    Hamid Ahaggach

Authors
  1. Hamid Ahaggach
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Corresponding author

Correspondence to Hamid Ahaggach .

Editor information

Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Tiago Prince Sales

  2. TU Wien, Vienna, Austria

    Henderik A. Proper

  3. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

  4. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Marco Montali

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Fabrizio Maria Maggi

  6. University of Twente, Enschede, The Netherlands

    Claudenir M. Fonseca

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Ahaggach, H. (2023). Data Analytics and Machine Learning for Smart Decision Making in Automotive Sector. In: Sales, T.P., Proper, H.A., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds) Enterprise Design, Operations, and Computing. EDOC 2022 Workshops . EDOC 2022. Lecture Notes in Business Information Processing, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-031-26886-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-26886-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26885-4

  • Online ISBN: 978-3-031-26886-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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