, Volume 99, Issue 2, pp 163–181 | Cite as

Situation recognition and handling based on executing situation templates and situation-aware workflows

  • Pascal HirmerEmail author
  • Matthias Wieland
  • Holger Schwarz
  • Bernhard Mitschang
  • Uwe Breitenbücher
  • Santiago Gómez Sáez
  • Frank Leymann


Today, the Internet of Things has evolved due to an advanced interconnectivity of hardware devices equipped with sensors and actuators. Such connected environments are nowadays well-known as smart environments. Famous examples are smart homes, smart cities, and smart factories. Such environments should only be called “smart” if they allow monitoring and self-organization. However, this is a great challenge: (1) sensors have to be bound and sensor data have to be efficiently provisioned to enable monitoring of these environments, (2) situations have to be detected based on sensor data, and (3) based on the recognized situations, a reaction has to be triggered to enable self-organization, e.g., through notification delivery or the execution of workflows. In this article, we introduce SitOPT—an approach for situation recognition based on raw sensor data and automated handling of occurring situations through notification delivery or execution of situation-aware workflows. This article is an extended version of the paper “SitRS—Situation Recognition based on Modeling and Executing Situation Templates” presented at the 9th Symposium and Summer School of Service-oriented Computing 2015.


Situation recognition IoT Context Integration Cloud computing Workflows Middleware 

Mathematics Subject Classification

68N01 68U35 68M11 


  1. 1.
    Attard, J., Scerri, S., Rivera, I., Handschuh, S.: Ontology-based situation recognition for context-aware systems. In: Proceedings of the 9th International Conference on Semantic Systems (2013)Google Scholar
  2. 2.
    Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput. Netw. 54(15):2787–2805CrossRefzbMATHGoogle Scholar
  3. 3.
    Breitenbücher, U., Hirmer, P., Képes, K., Kopp, O., Leymann, F., Wieland, M.: A situation-aware workflow modelling extension. In: Proceedings of iiWAS’15, pp. 478–484. ACM (2015)Google Scholar
  4. 4.
    Bucchiarone, A., Marconi, A., Pistore, M., Raik, H.: Dynamic adaptation of fragment-based and context-aware business processes. In: Proceedings of ICWS’12, pp. 33–41 (2012)Google Scholar
  5. 5.
    Buchmann, A., Koldehofe, B.: Complex event processing. it-Information Technology Methoden und innovative Anwendungen der Informatik und Informationstechnik (2009)Google Scholar
  6. 6.
    Dargie, W., Eldora, Mendez, J., Mobius, C., Rybina, K., Thost, V., Turhan, A.Y.: Situation Recognition for service management systems using OWL 2 reasoners. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) (2013)Google Scholar
  7. 7.
    Dey, A.K.: Understanding and using context. Personal and ubiquitous computing (2001)Google Scholar
  8. 8.
    Eberle, H., Unger, T., Leymann, F.: Process fragments. In: OTM’09, pp. 398–405. Springer (2009)Google Scholar
  9. 9.
    Fürst, S.: Konzept und Implementierung eines Situation Handlers. Master Thesis, University of Stuttgart, IAAS (2015)Google Scholar
  10. 10.
    Gómez Sáez, S., Andrikopoulos, V., Hahn, M., Karastoyanova, D., Weiß, A.: Enabling reusable and adaptive modeling, provisioning and execution of BPEL processes. In: Proceedings of IEEE SOCA’15 (2015)Google Scholar
  11. 11.
    González L, Ortiz G (2014) An event-driven integration platform for context-aware web services. J. UCS 20(8):1071–1088Google Scholar
  12. 12.
    Großmann, M., Bauer, M., Hönle, N., Käppeler, U.P., Nicklas, D., Schwarz, T.: Efficiently managing context information for large-scale scenarios. In: Proc. of the Third IEEE Intl. Conf. on Pervasive Computing and Communications (2005)Google Scholar
  13. 13.
    Han, J., Cho, Y., Choi, J.: Context-aware workflow language based on web services for ubiquitous computing, pp. 1008–1017. Springer (2005)Google Scholar
  14. 14.
    Häussermann, K., Hubig, C., Levi, P., Leymann, F., Siemoneit, O., Wieland, M., Zweigle, O.: Understanding and designing situation-aware mobile and ubiquitous computing systems. In: Proceedings of the International Conference on Computer, Electrical, and Systems Science, and Engineering 2010 (ICCESSE 2010) (2010)Google Scholar
  15. 15.
    Herrmann, K., Rothermel, K., Kortuem, G., Dulay, N.: Adaptable pervasive flows—an emerging technology for pervasive adaptation. In: Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, 2008. SASOW 2008, pp. 108–113 (2008)Google Scholar
  16. 16.
    Hirmer, P., Wieland, M., Breitenbücher, U., Mitschang, B.: Automated sensor registration, binding and sensor data provisioning. In: Proceedings of the CAiSE 2016 Forum at the 28th International Conference on Advanced Information Systems Engineering (2016)Google Scholar
  17. 17.
    Hirmer, P., Wieland, M., Breitenbücher, U., Mitschang, B.: Dynamic ontology-based sensor binding. In: Proceedings of the 20th East-European Conference on Advances in Databases and Information Systems (2016)Google Scholar
  18. 18.
    Hirmer, P., Wieland, M., Schwarz, H., Mitschang, B., Breitenbücher, U., Leymann, F.: SitRS—a situation recognition service based on modeling and executing situation templates. In: Proceedings of the 9th Symposium and Summer School On Service-Oriented Computing, pp. 113–127. IBM Research Report (2015)Google Scholar
  19. 19.
    Képes, K., Breitenbücher, U., Gómez Sáez, S., Guth, J., Leymann, F., Wieland, M.: Situation-aware execution and dynamic adaptation of traditional workflow models. In: Proceedings of the ESOCC 2016 (2016)Google Scholar
  20. 20.
    Lange, R., Cipriani, N., Geiger, L., Großmann, M., Weinschrott, H., Brodt, A., Wieland, M., Rizou, S., Rothermel, K.: Making the world wide space happen: new challenges for the nexus context platform. In: Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom ’09). Galveston, TX, USA. March 2009 (2009)Google Scholar
  21. 21.
    Leymann F, Roller D (2000) Production workflow: concepts and techniques. Prentice Hall PTR, Upper Saddle River, NJ, USAzbMATHGoogle Scholar
  22. 22.
    Meunier, R.: The pipes and filters architecture. In: Pattern languages of program design (1995)Google Scholar
  23. 23.
    Mormul, M., Hirmer, P., Wieland, M., Mitschang, B.: Situation model as interface between situation recognition and situation-aware applications. In: Proceedings of the 10th Symposium and Summer School On Service-Oriented Computing (2016)Google Scholar
  24. 24.
    Mundbrod, N., Grambow, G., Kolb, J., Reichert, M.: Context-aware process injection: enhancing process flexibility by late extension of process instances. In: Proceedings of CoopIS’15) (2015)Google Scholar
  25. 25.
    Franco da Silva, A.C., Hirmer, P., Wieland, M., Mitschang, B.: SitRS XT—towards near real time situation recognition. In: Proceedings of the 31st Brazilian Symposium of Databases (SBBD) (2016)Google Scholar
  26. 26.
    Vermesan, O., Friess, P.: Internet of Things: converging technologies for smart environments and integrated ecosystems. River Publishers (2013)Google Scholar
  27. 27.
    Wang, X., Zhang, D.Q., Gu, T., Pung, H.: Ontology based context modeling and reasoning using OWL. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops (2004)Google Scholar
  28. 28.
    Wieland, M., Kopp, O., Nicklas, D., Leymann, F.: Towards context-aware workflows. In: Pernici, B., Gulla, J.A. (eds.) Proceedings of CAiSE07, pp. 577–591. Tapir Acasemic Press (2007)Google Scholar
  29. 29.
    Wieland, M., Schwarz, H., Breitenbücher, U., Leymann, F.: Towards situation-aware adaptive workflows. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom) (2015)Google Scholar
  30. 30.
    Wieland, M., Schwarz, H., Breitenbücher, U., Leymann, F.: Towards situation-aware adaptive workflows: Sitopt—a general purpose situation-aware workflow management system. In: Proceedings of PerCom’15, pp. 32–37. IEEE (2015)Google Scholar
  31. 31.
    Wolf, H., Herrmann, K., Rothermel, K.: Modeling dynamic context awareness for situated workflows, pp. 98–107. Springer (2009)Google Scholar
  32. 32.
    Zweigle, O., Häussermann, K., Käppeler, U.P., Levi, P.: Supervised learning algorithm for automatic adaption of situation templates using uncertain data. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human (2009)Google Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Institute of Parallel and Distributed SystemsUniversity of StuttgartStuttgartGermany
  2. 2.Institute of Architecture of Application SystemsUniversity of StuttgartStuttgartGermany

Personalised recommendations