A Hybrid Modeling Method for Service-Oriented C4ISR Requirements Analysis

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 107)

Abstract

With the rapid growth of complexity as well as the continuously innovational concept of network-centric warfare, it is difficult to analyze and design C4ISR systems based on the existing component technology or the object-oriented technology. However, both the workflow method and the AI planning method for service modeling are not very suitable for analysis and design service-oriented C4ISR requirements. This paper imports Service-Oriented Computing (SOC) design paradigm to C4ISR requirements analysis, and proposes a hybrid modeling method based on multi-ontologies. Accompanied with an instance, the method provides a new, flexible, reusable solution for C4ISR requirements evolution and system reconstruction.

Keywords

SOC C4ISR Service modeling Ontology 

References

  1. 1.
    Huhns MN, Singh MP (2005) Service-oriented computing: key concepts and principles [J]. IEEE Internet Comput. (244):021–028Google Scholar
  2. 2.
    Benatallah B, Motahari Nezhad HR (2005) Service oriented computing: opportunities and challenges, LNCS, vol 3372, pp 1–8 Google Scholar
  3. 3.
    Papazoglou MP, Traverso P, Dustdar S et al (2008) Service-oriented computing: a research roadmap [J]. Int J Co-op Inf Syst 17(2):223–255Google Scholar
  4. 4.
    Papazoglou MP, Schmidt JW, Mylopoulos J (2009) Service-Oriented Computing [M]. MIT Press Cambridge Google Scholar
  5. 5.
    Wu B, Jin Z, Zhao B (2008) A modeling approach for service-oriented application [C]. In: Proceedings of the IEEE international conference on web servicesGoogle Scholar
  6. 6.
    Lee SY, Lee JY, Lee BI (2006) Service composition techniques using data mining for ubiquitous computing environments. Int J Comput Sci Netw Secur [J] 6(9):110–117Google Scholar
  7. 7.
    Ma Y, Jin B, Feng Y (2005) Dynamic discovery for semantic Web services based on evolving distributed ontologies [J]. Chin J Comput 28(4):603−615 (in Chinese with English abstract)Google Scholar
  8. 8.
    Qiu L, Shi Z, Lin F (2006) Context optimization of AI planning for services composition [C]. In: Proceedings of the IEEE international conference on e-business engineering, pp 610–617Google Scholar
  9. 9.
    Bottaro J, Bourcier C, Escoer et al (2007) Autonomic context-aware service composition [C]. In: Proceedings of 2nd IEEE international conference on pervasive servicesGoogle Scholar
  10. 10.
    Mingkhwan P, Fergus O, Abuelma’Atti et al (2006) Dynamic service composition in home appliance networks. Multimedia Tools Appl [J] 29(3):257−284Google Scholar
  11. 11.
    Viroli M, Denti E, Ricci A (2007) Engineering a BPEL orchestration engine as a multi-agent system [J]. Sci Comput Program 66:226–245CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Chi YL, Lee HM (2008) A formal modeling platform for composing web services [J]. Expert Syst Appl 34:1500–1507CrossRefGoogle Scholar
  13. 13.
    Valero V, Emilia Cambronero M, Díaz G et al (2009) A petri net approach for the design and analysis of Web Services Choreographies [J]. J Log Algebraic Progr 78:359–380Google Scholar
  14. 14.
    Deng S (2007) Research on automatic service composition and formal verification [D] (in Chinese). Ph.D thesis of Zhejiang UniversityGoogle Scholar
  15. 15.
    Liao S, Sun B, Wang R (2003) A knowledge-based architecture for planning military intelligence, surveillance, and reconnaissance [J]. Space Policy 19(3):191–202CrossRefGoogle Scholar
  16. 16.
    US Department of Defense. DoD Architecture Framework Version 2.0 [R]. 2009Google Scholar
  17. 17.
    Baader F, Calvanese D, Mcguinness DL et al (2003) The Description Handbook [M]. Cambridge University Press, CambridgeGoogle Scholar
  18. 18.
    Erl Thomas (2005) Service-oriented architecture (SOA):concepts, technology, and design [M], Pearson Education Google Scholar
  19. 19.
    Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language [J]. J Artif Intell Res 11:121–126Google Scholar
  20. 20.
    Chen L, Song Z, Zhang Y et al (2010) A method of semantic web service composition based on the canonical structure[J]. J Inf Comput Sci 7(2):463–469Google Scholar
  21. 21.
    Dong Q, Wang Ze, Chen J et al (2010) Method of checking capability model based on description logic [J]. Syst Eng Electron 32(3):533–539 (in Chinese with English abstract)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Ying ZHANG
    • 1
  • Xiaoming Liu
    • 1
  • Zhixue Wang
    • 1
  • Li Chen
    • 1
  1. 1.Institute of Command Automation, PLA University of Science and TechnologyNanjingChina

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