Advertisement

Digital Transformation Trends: Industry 4.0, Automation, and AI

Industrial Track at ISoLA 2018
  • Axel Hessenkämper
  • Falk HowarEmail author
  • Andreas Rausch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11247)

Abstract

The industrial track at ISoLA 2018 provided a platform for presenting industrial perspectives on digitalization and for discussing trends and challenges in the ongoing digital transformation. The track continued two special tracks at ISoLA conferences focused on the application of learning techniques in software engineering and software products [3], and industrial applications of formal methods in the context of Industry 4.0 [5]. Topics of interest included but were not limited to Industry 4.0, industrial applications of formal methods, and applications of machine-learning in industrial contexts.

References

  1. 1.
    Bosch, J., Olsson, H.H.: Data-driven continuous evolution of smart systems. In: Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2016, pp. 28–34. ACM, New York (2016)Google Scholar
  2. 2.
    Hagerer, A., Hungar, H., Niese, O., Steffen, B.: Model generation by moderated regular extrapolation. In: Kutsche, R.-D., Weber, H. (eds.) FASE 2002. LNCS, vol. 2306, pp. 80–95. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45923-5_6CrossRefGoogle Scholar
  3. 3.
    Howar, F., Meinke, K., Rausch, A.: Learning systems: machine-learning in software products and learning-based analysis of software systems. In: Margaria, T., Steffen, B. (eds.) ISoLA 2016. LNCS, vol. 9953, pp. 651–654. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47169-3_50CrossRefGoogle Scholar
  4. 4.
    Kagermann, H., Wahlster, W., Helbig, J.: Umsetzungsempfehlungen für das zukunftsprojekt industrie 4.0: Deutschlands zukunft als produktionsstandort sichern. Abschlussbericht des arbeitskreises industrie 4.0, acatech - Deutsche Akademie der Technikwissenschaften e. V., München, April 2013Google Scholar
  5. 5.
    Margaria, T., Steffen, B. (eds.): ISoLA 2016. LNCS, vol. 9953. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47169-3CrossRefGoogle Scholar
  6. 6.
    Meinke, K., Sindhu, M.A.: Incremental learning-based testing for reactive systems. In: Gogolla, M., Wolff, B. (eds.) TAP 2011. LNCS, vol. 6706, pp. 134–151. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21768-5_11CrossRefGoogle Scholar
  7. 7.
    Raffelt, H., Merten, M., Steffen, B., Margaria, T.: Dynamic testing via automata learning. Int. J. Softw. Tools Technol. Transf. 11(4), 307–324 (2009)CrossRefGoogle Scholar
  8. 8.
    Schäfer, T., et al.: A methodology for combinatory process synthesis: process variability in clinical pathways. In: Margaria, T., Steffen, B., (eds.) ISoLA 2018. LNCS, vol. 11247, pp. 472–486. Springer, Heidelberg (2018)Google Scholar
  9. 9.
    Stefffen, B., Bosselmann, S.: GOLD: global organization alignment and decision - towards the hierarchical integration of heterogeneous business models. In: Margaria, T., Steffen, B., (eds.) ISoLA 2018. LNCS, vol. 11247, pp. 504–527. Springer, Heidelberg (2018)Google Scholar
  10. 10.
    Winkels, J., Graefenstein, J., Schäfer, T., Scholz, D., Rehof, J., Henke, M.: Automatic composition of rough solution possibilities in the target planning of factory planning projects by means of combinatory logic. In: Margaria, T., Steffen, B., (eds.) ISoLA 2018. LNCS, vol. 11247, pp. 487–503. Springer, Heidelberg (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Axel Hessenkämper
    • 1
  • Falk Howar
    • 2
    Email author
  • Andreas Rausch
    • 3
  1. 1.Hottinger Baldwin Messtechnik GmbHDarmstadtGermany
  2. 2.Dortmund University of Technology and Fraunhofer ISSTDortmundGermany
  3. 3.Clausthal University of TechnologyClausthal-ZellerfeldGermany

Personalised recommendations