Advertisement

A Goal-Driven Context-Aware Architecture for Distributing Cognitive Service Group

  • Siyuan LuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)

Abstract

Cognitive service is an emerging service paradigm in service-oriented computing. It can comprehend data in the same way as the human. Cognitive services require sufficient information to understand service scenarios. Actually, to achieve a goal sometimes requires multiple services with order dependencies and prerequisites to work collaboratively. When an exception event occurs during the service group working, the conventional approach is to restart or stop the service based on the exception type. If the environment information is changed much fast and retrieved unpractically, the exception event can cause the delayed response of the service group. If the goal of the service group is time-aware and service result is preferred, the regular policy is hard to match the requirement. In this paper, we address the problem of delayed response caused by exception events raised from the distributing cognitive service group. A novel architecture is proposed to ensure the overall consistency and real-time reaction of distributing cognitive service group.

Keywords

Service-oriented computing Cognitive service Context awareness 

References

  1. 1.
    Microsoft Cognitive Service. https://azure.microsoft.com/en-us/services/cognitive-services/. Accessed 4 June 2018
  2. 2.
    Chan, A.T., Chuang, S.-N.: MobiPADS: a reflective middleware for context-aware mobile computing. IEEE Trans. Softw. Eng. 29(12), 1072–1085 (2003)CrossRefGoogle Scholar
  3. 3.
    Gu, T., Pung, H.K., Zhang, D.Q.: A service-oriented middleware for building context-aware services. J. Netw. Comput. Appl. 28(1), 1–18 (2005)CrossRefGoogle Scholar
  4. 4.
    Santos, L.O., Poortinga, R., Vink, P.: A service-oriented middleware for context-aware applications. In: Proceedings of 5th International Workshop on Middleware for Pervasive and Ad-Hoc Computing (2007)Google Scholar
  5. 5.
    Jong-yi, H., Eui-ho, S., Sung-Jin, K.: Context-aware systems: a literature review and classification. Expert Syst. 36(4), 8509–8522 (2009)CrossRefGoogle Scholar
  6. 6.
    Eisenhauer, M., Rosengren, P., Antolin, P.: HYDRA: a development platform for integrating wireless devices and sensors into ambient intelligence systems. The Internet of Things, pp. 367–373. Springer, New York (2010).  https://doi.org/10.1007/978-1-4419-1674-7_36CrossRefGoogle Scholar
  7. 7.
    Kabir, M.A., Han, J., Yu, J., Colman, A.: SCIMS: a social context information management system for socially-aware applications. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 301–317. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31095-9_20CrossRefGoogle Scholar
  8. 8.
    Khan, A.J., Jayarajah, K., Han, D., Misra, A., Balan, R., Seshan, S.: CAMEO: a middleware for mobile advertisement delivery. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 125–138. ACM (2013)Google Scholar
  9. 9.
    Charith, P., Arkady, Z., Christen, P., Dimitrios, G.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014)
Google Scholar
  10. 10.
    Cabrera, O., Franch, X., Marco, J.: Ontology-based context modeling in service-oriented computing: a systematic mapping. Data Knowl. 110, 24–53 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina

Personalised recommendations