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Behavior Ontology to Model Collective Behavior of Emergency Medical Systems

  • Junsup Song
  • Maryam Rahmani
  • Moonkun LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10651)

Abstract

It is very important to understand system behaviors in collective pattern for each knowledge domain. However, there are structural limitations to represent collective behaviors due to the size of system components and the complexity of their interactions, causing the state explosion problem. Further composition with other systems is mostly impractical due to exponential growth of their size and complexity. This paper presents a practical method to model the collective behaviors, based on a new concept of domain engineering: behavior ontology. Two domains are selected to demonstrate the method: Emergency Medical Service (EMS) and Health Care Service (HCS) systems. The examples show that the method is very effective and efficient to construct a hierarchy of collective behaviors in a lattice and that the composition of two collective behaviors is systematically performed by the composition operation of two lattices. The method can be one of the most innovative approaches in representing system behaviors in collective pattern, as well as in minimization of system states to reduce system complexity. For implementation, a prototype tool, called PRISM, has been developed on ADOxx Meta-Modelling Platform.

Keywords

Collective behavior Behavior ontology PRISM ADOxx 

References

  1. 1.
    Choi, W., Choe, Y., Lee, M.: A reduction method for process and system complexity with conjunctive and complement choices in a process algebra. In: 39th IEEE COMPSAC/MVDM, July 2015Google Scholar
  2. 2.
    Woo, S., On, J., Lee, M.: An abstraction method for mobility, and interaction in process algebra using behavior ontology. In: 37th IEEE COMPSAC, July 2013Google Scholar
  3. 3.
    Choe, Y., Lee, M.: A Lattice model to verify behavioral equivalence. In: UKSim-AMSS 8th European Modelling Symposium, October 2014Google Scholar
  4. 4.
    Fill, H., Karagiannis, D.: On the conceptualisation of modeling methods using the ADOxx meta modeling platform. Enterp. Model. Inf. Syst. Architect. 8(1), 4–25 (2013)CrossRefGoogle Scholar
  5. 5.
    Clarke, E., Emerson, A., Sifakis, J.: Model checking: algorithmic verification and debugging. Commun. ACM 52(11), 74–84 (2009)CrossRefGoogle Scholar
  6. 6.
    Yeh, W., Young, M.: Compositional reachability analysis using process algebra. In: Proceedings of Conference on Testing, Analysis and Verification, pp. 49–59, August 1992Google Scholar
  7. 7.
    Chen, T., Chilton, C., Jonsson, B., Kwiatkowska, M.: A compositional specification theory for component behaviours. In: Seidl, H. (ed.) ESOP 2012. LNCS, vol. 7211, pp. 148–168. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28869-2_8 CrossRefGoogle Scholar
  8. 8.
    Raju, S.: An automatic verification technique for communicating real-time state machines. Technical report 93-07-08, Department of Computer science and Engineering, University of Washington, April 1993Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Chonbuk National UniversityJeonju-si JeonjuRepublic of Korea

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