Advertisement

Analysis Method for Conceptual Context Modeling Applied in Production Environments

  • Eva HoosEmail author
  • Matthias Wieland
  • Bernhard Mitschang
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 288)

Abstract

Context-awareness is a well-accepted approach to adapt applications to the needs of a user. Yet, it is hardly used in enterprise information systems, especially in production environments. Production environments are complex due to multi-faced actors, products, manufacturing equipment and processes. Hence, the modeling of context is sophisticated, particularly to determine relevant context. The goal of this paper is to facilitate and support context modeling in production environments. Our contribution is an analysis method for conceptual context modeling suited for Industry 4.0 and an extensible engineering context model as starting point for modeling of different Industry 4.0 use cases. The analysis model consists of a graphical notation for a simplified and abstract context model and a template-based concept to detail the model. Furthermore, we evaluate the approach by modeling a real use case from the car manufacturing industry.

Keywords

Context-awareness Production environments Industry 4.0 

References

  1. 1.
    Lee, A.N., Lastra, J.L.M.: Enhancement of industrial monitoring systems by utilizing context awareness. In: CogSIMA 2013, pp. 277–284 (2013)Google Scholar
  2. 2.
    Alexopoulos, K., et al.: A concept for context-aware computing in manufacturing: the white goods case. Int. J. Comput. Integr. Manufact. 29, 839–849 (2016)CrossRefGoogle Scholar
  3. 3.
    El Kadiri, S., et al.: Current trends on ICT technologies for enterprise information systems. Comput. Ind. 79, 14–33 (2016)CrossRefGoogle Scholar
  4. 4.
    Xie, Y., et al.: Opportunities and challenges for context-aware systems in aerospace industry. J. Enterp. Inf. Manage. 24(2), 118–125 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Perera, C., et al.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutorials 16(1), 414–454 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Henricksen, K., Indulska, J., McFadden, T.: Modelling context information with ORM. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2005. LNCS, vol. 3762, pp. 626–635. Springer, Heidelberg (2005). doi: 10.1007/11575863_82 CrossRefGoogle Scholar
  7. 7.
    Achilleos, A., et al.: Context modelling and a context-aware framework for pervasive service creation: a model-driven approach. Pervasive Mob. Comput. 6(2), 281–296 (2010)CrossRefGoogle Scholar
  8. 8.
    Reichle, R., Wagner, M., Khan, M.U., Geihs, K., Lorenzo, J., Valla, M., Fra, C., Paspallis, N., Papadopoulos, G.A.: A comprehensive context modeling framework for pervasive computing systems. In: Meier, R., Terzis, S. (eds.) DAIS 2008. LNCS, vol. 5053, pp. 281–295. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-68642-2_23 CrossRefGoogle Scholar
  9. 9.
    Bettini, C., et al.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)CrossRefGoogle Scholar
  10. 10.
    Dey, A.K., et al.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16(2–4), 97–166 (2001)CrossRefGoogle Scholar
  11. 11.
    Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS, vol. 4635, pp. 558–571. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-74255-5_42 CrossRefGoogle Scholar
  12. 12.
    Gröger, C., et al.: The data-driven factory - leveraging big industrial data for agile, learning and human-centric manufacturing. In: ICEIS 2016, pp. 40–52 (2016)Google Scholar
  13. 13.
    Moody, D.: The “physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6), 756–779 (2009)CrossRefGoogle Scholar
  14. 14.
    Henricksen, K., Indulska, J., Rakotonirainy, A.: Modeling context information in pervasive computing systems. In: Mattern, F., Naghshineh, M. (eds.) Pervasive 2002. LNCS, vol. 2414, pp. 167–180. Springer, Heidelberg (2002). doi: 10.1007/3-540-45866-2_14 CrossRefGoogle Scholar
  15. 15.
    Häussermann, K., et al.: Understanding and designing situation-aware mobile and ubiquitous computing systems. World Acad. Sci. Eng. Technol. 14(1), 329–339 (2010)Google Scholar
  16. 16.
    Hoos, E., Gröger, C., Kramer, S., Mitschang, B.: ValueApping: an analysis method to identify value-adding mobile enterprise apps in business processes. In: Cordeiro, J., Hammoudi, S., Maciaszek, L., Camp, O., Filipe, J. (eds.) ICEIS 2014. LNBIP, vol. 227, pp. 222–243. Springer, Cham (2015). doi: 10.1007/978-3-319-22348-3_13 CrossRefGoogle Scholar
  17. 17.
    Kofod-Petersen, A., Cassens, J.: Using activity theory to model context awareness. In: Roth-Berghofer, T.R., Schulz, S., Leake, D.B. (eds.) MRC 2005. LNCS, vol. 3946, pp. 1–17. Springer, Heidelberg (2006). doi: 10.1007/11740674_1 CrossRefGoogle Scholar
  18. 18.
    Costa, P.D., et al.: Towards conceptual foundations for context-aware applications. In: AAAI Workshop on Modeling and Retrieval of Context, pp. 54–58 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Eva Hoos
    • 1
    • 3
    Email author
  • Matthias Wieland
    • 2
  • Bernhard Mitschang
    • 2
    • 3
  1. 1.Daimler AGBöblingenGermany
  2. 2.Institute of Parallel and Distributed SystemsUniversity of StuttgartStuttgartGermany
  3. 3.Graduate School of Excellence Advanced Manufacturing EngineeringUniversity of StuttgartStuttgartGermany

Personalised recommendations