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agriOpenLink: Towards Adaptive Agricultural Processes Enabled by Open Interfaces, Linked Data and Services

  • Slobodanka Dana Kathrin Tomic
  • Anna Fensel
  • Christian Aschauer
  • Klemens Gregor Schulmeister
  • Thomas Riegler
  • Franz Handler
  • Marcel Otte
  • Wolfgang Auer
Part of the Communications in Computer and Information Science book series (CCIS, volume 390)

Abstract

Today, users involved in agricultural production processes increasingly rely on advanced agricultural machines and specialized applications utilizing the latest advances in information and communication technology (ICT). Robots and machines host numerous specialized sensors and measurement devices and generate large amounts of data that combined with data coming from external sources, could provide a basis for better process understanding and process optimization. One serious roadblock to this vision is a lack of interoperability between the equipment of different vendors; another pitfall of current solutions is that the process knowledge is not modelled in a standardized machine readable form. On the other hand, such process model can be flexibly used to support process-specific integration of machines, and enable context-sensitive automatic process optimization. This paper presents an approach and preliminary results regarding architecture for adaptive optimization of agricultural processes via open interfaces, linked data and semantic services that is being developed within the project agriOpenLink; its goal is to provide a novel methodology and tools for semantic proces orchestraion and dynamic context-based adaptation, significantly reducing the effort needed to create new ICT-controlled agricultural applications involving machines and users.

Keywords

Semantic Services Semantic Processes Ontology Open Interfaces 

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References

  1. 1.
    Roussey, C., et al.: Ontologies in Agriculture. Ingénierie des Systèmes D’Information 16(3), 55–84 (2011)CrossRefGoogle Scholar
  2. 2.
    Goumopoulos, C., et al.: An Ontology-Driven System Architecture for Precision Agriculture Applications. International Journal of Metadata, Semantics and Ontologies (IJMSO), 72–84 (2009)Google Scholar
  3. 3.
    ISO17532: Stationary equipment for agriculture - Data communications network for live-stock farming. Beuth Verlag, Genf.Google Scholar
  4. 4.
    Fensel, D., et al.: Enabling Semantic Web Services: The Web Service Modeling Ontology. Springer (2006)Google Scholar
  5. 5.
    Roman, D., et al.: Web Service Modeling Ontology. Applied Ontology 1(1), 77–106 (2005)Google Scholar
  6. 6.
    de Bruijn, J., Lausen, H., Polleres, A., Fensel, D.: The web service modeling language WSML: An overview. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 590–604. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Vitvar, T., Kopecký, J., Viskova, J., Fensel, D.: WSMO-Lite Annotations for Web Services. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 674–689. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Verborgh, R., et al.: Efficient Runtime Service Discovery and Consumption with Hyperlinked RESTdesc. In: 7th International Conference on Next Generation Web Services Practices (2011)Google Scholar
  9. 9.
    Pedrinaci, C., et al.: Services and the Web of Data: An Unexploited Symbiosis. In: AAAI Spring Symposium (March 2010)Google Scholar
  10. 10.
    McCown, et al.: APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems 50(3), 255–271 (1996)CrossRefGoogle Scholar
  11. 11.
    Ebadian, M., et al.: A new simulation model for multi-agricultural biomass logistics system in bioenergy production. Biosystems Engineering 110(3), 280–290 (2011)CrossRefGoogle Scholar
  12. 12.
    Alan, A.: A combined continuous - discrete FORTRAN-based simulation language. School of Industrial Engineering Purdue University LafayetteGoogle Scholar
  13. 13.
    Feldkamp, D., Singh, N.: Making BPEL flexible. Technical Report SS-08-01, Association for the Advancement of Artificial Intelligence (2008)Google Scholar
  14. 14.
    Passant, A., et al.: sparqlPuSH: Proactive notification of data updates in RDF stores using PubSubHubbub. In: 6th Workshop on Scripting and Development for the Semantic Web, Crete, Greece (May 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Slobodanka Dana Kathrin Tomic
    • 1
  • Anna Fensel
    • 1
  • Christian Aschauer
    • 2
  • Klemens Gregor Schulmeister
    • 2
  • Thomas Riegler
    • 3
  • Franz Handler
    • 3
  • Marcel Otte
    • 4
  • Wolfgang Auer
    • 4
  1. 1.The Telecommunications Research Center Vienna (FTW)ViennaAustria
  2. 2.Division of Agricultural Engineering (BOKU)University of Natural Resources and Life SciencesViennaAustria
  3. 3.Josephinum Research (JR)WieselburgAustria
  4. 4.MKW Electronics GesmbH (MKWE) WeibernAustria

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