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The next evolution of MDE: a seamless integration of machine learning into domain modeling

  • Thomas Hartmann
  • Assaad Moawad
  • Francois Fouquet
  • Yves Le Traon
Regular Paper

Abstract

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems and Internet of Things applications. Instead, to make smart decisions, such systems have to continuously refine the behavior on a per-element basis and compose these small learning units together. However, combining and composing learned behaviors from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviors and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as microlearning units. These microlearning units are modeled together with and at the same level as the domain data. We show, based on a smart grid case study, that our approach can be significantly more accurate than learning a global behavior, while the performance is fast enough to be used for live learning.

Keywords

Domain modeling Live learning Model-driven engineering Metamodeling Cyber-physical systems Smart grids 

Notes

Acknowledgements

Funding was provided by National Research Fund Luxembourg (Grant No. 6816126).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Thomas Hartmann
    • 1
  • Assaad Moawad
    • 2
  • Francois Fouquet
    • 1
  • Yves Le Traon
    • 1
  1. 1.University of LuxembourgLuxembourgLuxembourg
  2. 2.DataThings S.A.R.LLuxembourgLuxembourg

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