A Service Delivery Framework to Support Opportunistic Collaborations

  • Gregory Katsaros
  • Erik Wittern
  • Birgit Gray
  • Stefan Tai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8135)

Abstract

The wide spread use of computing devices, such as smart phones, cameras, and sensors results in abundance of available information. When such information flows occur in a specific place, at a certain time, and with the participating entities working together or sharing information to achieve common goals, we refer to the outcome of an opportunistic collaboration. In this paper we define and analyse this new collaboration domain and present a framework through which opportunistic collaboration services can be provisioned. We describe in detail the processes that the framework supports, including the modeling of opportunistic collaborations, the collaboration service creation, and the participation management. We evaluate the framework through a use case scenario in the context of participatory journalism in high-profile news events.

Keywords

opportunistic collaborations services collaboration model-driven engineering Cloud platform 

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References

  1. 1.
    Camarinha-Matos, L., Afsarmanesh, H.: Taxonomy of Collaborative Networks. Technical report, Future Internet Enterprise Systems (FInES) Task Force on Collaborative Networks (March 2012)Google Scholar
  2. 2.
    Kortuem, G., Kawsar, F., Sundramoorthy, V., Fitton, D.: Smart objects as building blocks for the internet of things. IEEE Internet Computing 14(1), 44–51 (2010)CrossRefGoogle Scholar
  3. 3.
    Leimeister, J.M., Huber, M., Bretschneider, U., Krcmar, H.: Leveraging crowdsourcing: Activation-supporting components for it-based ideas competition. J. Manage. Inf. Syst. 26(1), 197–224 (2009)CrossRefGoogle Scholar
  4. 4.
    OMG: Object Constraint Language v2.0. Technical report, Object Management Group (2006)Google Scholar
  5. 5.
    Mirko, L.: Status and Outlook for data-driven journalism. European Journalism Center: Data-Driven Journalism: What is there to learn? A Paper on the Data-Driven Journalism Roundtable Held in Amsterdam on 24 August (2010)Google Scholar
  6. 6.
    Poltrock, S., Handel, M.: Modeling Collaborative Behavior: Foundations for Collaboration Technologies. In: Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS), pp. 1–10 (2009)Google Scholar
  7. 7.
    Camarinha-Matos, L., Afsarmanesh, H.: The ARCON modeling framework. In: Collaborative Networks: Reference Modeling. Springer, New York (2008)Google Scholar
  8. 8.
    Huth, C., Smolnik, S., Nastansky, L.: Applying topic maps to ad hoc workflows for semantic associative navigation in process networks. In: Proceedings of the Seventh International Workshop on Groupware, pp. 44–49 (2001)Google Scholar
  9. 9.
    Groba, C.: Towards opportunistic service composition in dynamic ad hoc environments. In: Pallis, G., et al. (eds.) ICSOC 2011 Workshops. LNCS, vol. 7221, pp. 189–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Biswas, P.K., Qi, H., Xu, Y.: A Mobile-Agent-Based Collaborative Framework for Sensor Network Applications. In: 2006 IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), pp. 650–655 (October 2006)Google Scholar
  11. 11.
    Chen, F., Ren, C., Dong, J., Wang, Q., Li, J., Shao, B.: A Comprehensive Device Collaboration Model for Integrating Devices with Web Services under Internet of Things. In: Proceedings of the 19th IEEE International Conference on Web Services (ICWS). IEEE Computer Society, Los Alamitos (2011)Google Scholar
  12. 12.
    Hamadache, K., Lancieri, L.: Role-Based Collaboration Extended to Pervasive Computing. In: International Conference on Intelligent Networking and Collaborative Systems (INCOS 2009), pp. 9–15. IEEE Computer Society (November 2009)Google Scholar
  13. 13.
    Hamadache, K., Lancieri, L.: Dealing with device collaboration rules for the PCSCW model. In: Kolfschoten, G., Herrmann, T., Lukosch, S. (eds.) CRIWG 2010. LNCS, vol. 6257, pp. 233–248. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Koukoumidis, E., Martonosi, M., Peh, L.S.: Leveraging Smartphone Cameras for Collaborative Road Advisories. IEEE Transactions on Mobile Computing 11(5), 707–723 (2012)CrossRefGoogle Scholar
  15. 15.
    Liu, C.H., Hui, P.: Mobile Sensing for Social Collaborations. Technical report, Deutsche Telekom Laboratories (2011)Google Scholar
  16. 16.
    Schmidt, D.C.: Model-Driven Engineering. IEEE Internet Computing 39(2), 25–31 (2006)CrossRefGoogle Scholar
  17. 17.
    Sommerville, I.: Software Engineering, 9th edn. Addison-Wesley (2011)Google Scholar
  18. 18.
    Bruneliere, H., Cabot, J., Jouault, F., et al.: Combining Model-Driven Engineering and Cloud Computing. In: Modeling, Design, and Analysis for the Service Cloud-MDA4ServiceCloud 2010: Workshop’s 4th edn. (Co-Located with the 6th European Conference on Modelling Foundations and Applications-ECMFA 2010) (2010)Google Scholar
  19. 19.
    Le Nhan, T., Sunyé, G., Jézéquel, J.-M.: A Model-Driven Approach for Virtual Machine Image Provisioning in Cloud Computing. In: De Paoli, F., Pimentel, E., Zavattaro, G. (eds.) ESOCC 2012. LNCS, vol. 7592, pp. 107–121. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Ardagna, D., Di Nitto, E., Mohagheghi, P., Mosser, S., Ballagny, C., D’Andria, F., Casale, G., Matthews, P., Nechifor, C.S., Petcu, D., et al.: MODAClouds: A model-driven approach for the design and execution of applications on multiple Clouds. In: 2012 ICSE Workshop on Modeling in Software Engineering (MISE). IEEE Computer Society (2012)Google Scholar
  21. 21.
    Mikkonen, T., Nieminen, A.: Elements for a cloud-based development environment: online collaboration, revision control, and continuous integration. In: Proceedings of the WICSA/ECSA 2012 Companion Volume, WICSA/ECSA 2012. ACM, New York (2012)Google Scholar
  22. 22.
    Guinard, D., Floerkemeier, C., Sarma, S.: Cloud Computing, REST and Mashups to Simplify RFID Application Development and Deployment. In: Proceedings of the 2nd International Workshop on the Web of Things (WoT 2011). ACM, San Francisco (2011)CrossRefGoogle Scholar
  23. 23.
    Biehl, M., Gu, W., Loiret, F.: Model-based service discovery and orchestration for OSLC services in tool chains. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 283–290. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gregory Katsaros
    • 1
  • Erik Wittern
    • 1
  • Birgit Gray
    • 2
  • Stefan Tai
    • 1
  1. 1.FZI - Research Center for Information TechnologyBerlinGermany
  2. 2.DW - Deutsche WelleGermany

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