Software & Systems Modeling

, Volume 18, Issue 2, pp 1117–1134 | Cite as

Toward a framework for self-adaptive workflows in cyber-physical systems

  • Ronny SeigerEmail author
  • Steffen Huber
  • Peter Heisig
  • Uwe Aßmann
Special Section Paper


With the establishment of Cyber-physical Systems (CPS) and the Internet of Things, the virtual world of software and services and the physical world of objects and humans move closer together. Despite being a useful means for automation, BPM technologies and workflow systems are yet not fully capable of executing processes in CPS. The effects on and possible errors and inconsistencies in the physical world are not considered by “traditional” workflow engines. In this work we propose a framework for self-adaptive workflows in CPS based on the MAPE-K feedback loop. Within this loop monitoring and analysis of additional sensor and context data is used to check for unanticipated errors in the physical world. Planning and execution of compensation actions restores Cyber-physical Consistency, which leads to an increased resilience of the process execution environment. The framework facilitates the separation of CPS aspects from the “regular” workflow views. We show the feasibility of this approach in a smart home scenario and discuss the application of our approach for legacy BPM systems.


Workflows for the Internet of Things Cyber-physical Systems Self-adaptive Workflows Real-world processes Cyber-physical consistency 



This research has received funding under the Grant Number 100268299 (“CyPhyMan” project) by the European Social Fund (ESF) and the German Federal State of Saxony. Kudos to André Kühnert for helping with the implementations.


  1. 1.
    Aalst, W.M.P., Hofstede, A.H.M., Weske, M.: Business Process Management. In: International Conference, BPM 2003 Eindhoven, The Netherlands, June 26–27, 2003 Proceedings, Chapter Business Process Management: A Survey, pp. 1–12. Springer, Berlin (2003)Google Scholar
  2. 2.
    Andonoff, E., Bouaziz, W., Hanachi, C., Bouzguenda, L.: An agent-based model for autonomic coordination of inter-organizational business processes. Informatica 20(3), 323–342 (2009)Google Scholar
  3. 3.
    Baumgrass, A., Di Ciccio, C., Dijkman, R. M., Hewelt, M., Mendling, J., Meyer, A., Wong, T. Y. GET controller and UNICORN: event-driven process execution and monitoring in logistics. In: BPM (Demos), pp. 75–79 (2015)Google Scholar
  4. 4.
    Bonino, D., Corno, F.: Dogont-ontology modeling for intelligent domotic environments. In: Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) The Semantic Web-ISWC 2008, Lecture Notes in Computer Science, vol. 5318, pp. 790–803. Springer, Berlin (2008)Google Scholar
  5. 5.
    Braberman, V., D’Ippolito, N., Kramer, J., Sykes, D., Uchitel, S.: Morph: a reference architecture for configuration and behaviour self-adaptation. In: Proceedings of the 1st International Workshop on Control Theory for Software Engineering, pp. 9–16. ACM (2015)Google Scholar
  6. 6.
    Brun, Y., Serugendo, G.D.M., Gacek, C., Giese, H., Kienle, H., Litoiu, M., Müller, H., Pezzè, M., Shaw, M.: Engineering self-adaptive systems through feedback loops. In: Software engineering for self-adaptive systems, pp. 48–70. Springer, Berlin (2009)Google Scholar
  7. 7.
    Conti, M., Das, S.K., Bisdikian, C., Kumar, M., Ni, L.M., Passarella, A., Roussos, G., Trster, G., Tsudik, G., Zambonelli, F.: Looking ahead in pervasive computing: challenges and opportunities in the era of cyberphysical convergence. Pervasive Mob. Comput. 8(1), 2–21 (2012)CrossRefGoogle Scholar
  8. 8.
    Dar, K., Taherkordi, A., Baraki, H., Eliassen, F., Geihs, K.: A resource oriented integration architecture for the internet of things: a business process perspective. Pervasive Mob. Comput. 20, 145–159 (2015)CrossRefGoogle Scholar
  9. 9.
    De Lemos, R., Giese, H., Müller, H.A., Shaw, M., Andersson, J., Litoiu, M., Schmerl, B., Tamura, G., Villegas, N.M., Vogel, T., et al.: Software Engineering for Self-Adaptive Systems: A Second Research Roadmap. Springer, Berlin (2013)CrossRefGoogle Scholar
  10. 10.
    Frincu, M.E.: D-OSyRIS: a self-healing distributed workflow engine. In: International Symposium on Parallel and Distributed Computing, pp. 215–222 (2011)Google Scholar
  11. 11.
    Glombitza, N., Ebers, S., Pfisterer, D., Fischer, S.: Using BPEL to realize business processes for an internet of things. In: International Conference on Ad-Hoc Networks and Wireless, pp. 294–307. Springer (2011)Google Scholar
  12. 12.
    Graja, I., Kallel, S., Guermouche, N., Kacem, A.H.: BPMN4CPS: A BPMN extension for modeling cyber-physical systems. In: 2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 152–157 (2016)Google Scholar
  13. 13.
    Gray, J., Reuter, A.: Transaction Processing: Concepts and Techniques. Elsevier, Amsterdam (1992)zbMATHGoogle Scholar
  14. 14.
    Guinard, D., Ion, I., Mayer, S.: In search of an internet of things service architecture: Rest or ws-*? A developers perspective. In: International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, pp. 326–337. Springer (2011)Google Scholar
  15. 15.
    Gurgen, L., Gunalp, O., Benazzouz, Y., Gallissot, M.: Self-aware cyber-physical systems and applications in smart buildings and cities. In: Proceedings of the Conference on Design, Automation and Test in Europe (DATE ’13), pp. 1149–1154. EDA Consortium, San Jose (2013)Google Scholar
  16. 16.
    Herzberg, N., Meyer, a., Weske, M.: An event processing platform for business process management. In: 17th IEEE International Enterprise Distributed Object Computing Conference, pp. 107–116 (2013)Google Scholar
  17. 17.
    Hirmer, P., Wieland, M., Schwarz, H., Mitschang, B., Breitenbücher, U., Sáez, S.G., Leymann, F.: Situation recognition and handling based on executing situation templates and situation-aware workflows. Computing 99(2), 1–19 (2016)MathSciNetGoogle Scholar
  18. 18.
    Huber, S., Seiger, R., Kühnert, A., Schlegel, T.: A context-adaptive workflow engine for humans, things and services. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp ’16), pp. 285–288. ACM, New York (2016)Google Scholar
  19. 19.
    Huber, S., Seiger, R., Schlegel, T.: Using semantic queries to enable dynamic service invocation for processes in the internet of things. In: 2016 IEEE International Conference on Semantic Computing (ICSC), pp. 214–221 (2016)Google Scholar
  20. 20.
    Kephart, J., Kephart, J., Chess, D., Boutilier, C., Das, R., Kephart, J.O., Walsh, W.E.: An architectural blueprint for autonomic computing. In: IBM (2003)Google Scholar
  21. 21.
    Kim, M., Ahn, H., Kim, K.P.: Process-aware internet of things: a conceptual extension of the internet of things framework and architecture. KSII Trans. Internet Inf. Syst. 10(8), 4008–4022 (2016)Google Scholar
  22. 22.
    Koetter, F., Kochanowski, M.: Business Information Systems. In: 15th International Conference, BIS 2012, Vilnius, Lithuania, May 21–23, 2012. Proceedings, Chapter Goal-Oriented Model-Driven Business Process Monitoring Using ProGoalML, pp. 72–83. Springer, Berlin (2012)Google Scholar
  23. 23.
    Kopetz, H.: System-of-systems complexity. arXiv preprint arXiv:1311.3629 (2013)
  24. 24.
    Kourtesis, D., Paraskakis, I.: Combining SAWSDL, OWL-DL and UDDI for semantically enhanced web service discovery. Semant. Web Res. Appl. 614–628 (2008)Google Scholar
  25. 25.
    Kramer, J., Magee, J.: Self-managed systems: an architectural challenge. In: Future of Software Engineering (FOSE’07), pp. 259–268. IEEE (2007)Google Scholar
  26. 26.
    Lee, E.: Cyber physical systems: design challenges. In: 2008 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)Google Scholar
  27. 27.
    Leotta, F., Mecella, M., Mendling, J.: Applying process mining to smart spaces: perspectives and research challenges. In: Advanced Information Systems Engineering Workshops, pp. 298–304. Springer (2015)Google Scholar
  28. 28.
    Marrella, A., Mecella, M., Sardina, S.: SmartPM: an adaptive process management system through situation calculus, indigolog, and classical planning. In: Principles of Knowledge Representation and Reasoning, pp. 1–10. AAAI Press, Menlo Park (2014)Google Scholar
  29. 29.
    Marrella, A., Mecella, M., Sardina, S.: Intelligent process adaptation in the SmartPM system. ACM Trans. Intell. Syst. Technol. 8(2), 25:1–25:43 (2016)CrossRefGoogle Scholar
  30. 30.
    Meyer, S., Ruppen, A., Hilty, L.: The things of the internet of things in BPMN. In: Advanced Information Systems Engineering Workshops, pp. 285–297 (2015)Google Scholar
  31. 31.
    Meyer, S., Ruppen, A., Magerkurth, C.: Internet of things-aware process modeling. In: Integrating IoT devices as business process resources. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7908 LNCS, pp. 84–98 (2013)Google Scholar
  32. 32.
    Oliveira, K., Castro, J., España, S., Pastor, O.: Multi-level autonomic business process management. Enterp. Bus. Process Inf. Syst. Model. 184–198 (2013)Google Scholar
  33. 33.
    Perrin, O., Godart, C.: A model to support collaborative work in virtual enterprises. Data Knowl. Eng. 50(1), 63–86 (2004). (advances in business process management)CrossRefGoogle Scholar
  34. 34.
    Piechnick, C., Richly, S., Kühn, T., Götz, S., Püschel, G., Aßmann, U.: Contextpoint: an architecture for extrinsic meta-adaptation in smart environments. In: Sixth International Conference on Adaptive and Self-adaptive Systems and Applications, pp. 121–128 (2014)Google Scholar
  35. 35.
    de Roo, A., Sozer, H., Aksit, M.: Runtime verification of domain-specific models of physical characteristics in control software. In: 2011 Fifth International Conference on Secure Software Integration and Reliability Improvement, pp. 41–50 (2011)Google Scholar
  36. 36.
    Rouvoy, R., Barone, P., Ding, Y., Eliassen, F., Hallsteinsen, S., Lorenzo, J., Mamelli, A., Scholz, U.: Music: middleware support for self-adaptation in ubiquitous and service-oriented environments. In: Software engineering for self-adaptive systems, pp. 164–182. Springer (2009)Google Scholar
  37. 37.
    Saidani, O., Rolland, C., Nurcan, S.: Towards a generic context model for BPM. In: 2015 48th Hawaii International Conference on System Sciences (HICSS), pp. 4120–4129 (2015)Google Scholar
  38. 38.
    Seiger, R., Huber, S., Heisig, P., Assmann, U.: Enabling Self-adaptive Workflows for Cyber-physical Systems, pp. 3–17. Springer, Berlin (2016)Google Scholar
  39. 39.
    Seiger, R., Huber, S., Schlegel, T.: Proteus: an integrated system for process execution in cyber-physical systems. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) Enterprise, Business-Process and Information Systems Modeling, Lecture Notes in Business Information Processing, vol. 214, pp. 265–280 (2015)Google Scholar
  40. 40.
    Seiger, R., Huber, S., Schlegel, T.: Toward an execution system for self-healing workflows in cyber-physical systems. Softw. Syst. Model. 1–22 (2016) (special section paper) Google Scholar
  41. 41.
    Seiger, R., Keller, C., Niebling, F., Schlegel, T.: Modelling complex and flexible processes for smart cyber-physical environments. J. Comput. Sci. 10, 137–148 (2015)CrossRefGoogle Scholar
  42. 42.
    Seiger, R., Niebling, F., Schlegel, T.: A distributed execution environment enabling resilient processes for ubiquitous systems. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 220–223 (2014)Google Scholar
  43. 43.
    Smirek, L., Zimmermann, G., Ziegler, D.: Towards universally usable smart homes-how can MyUI, URC and openHAB contribute to an adaptive user interface platform. In: IARIA, Nice, France, pp. 29–38 (2014)Google Scholar
  44. 44.
    Stork, A.: Visual computing challenges of advanced manufacturing and industrie 4.0 [guest editors’ introduction]. IEEE Comput. Graphics Appl. 35(2), 21–25 (2015)CrossRefGoogle Scholar
  45. 45.
    Talcott, C.: Cyber-physical systems and events. In: Software-Intensive Systems and New Computing Paradigms, pp. 101–115. Springer (2008)Google Scholar
  46. 46.
    Webber, J.: A programmatic introduction to neo4j. In: Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Software for Humanity, pp. 217–218. ACM (2012)Google Scholar
  47. 47.
    Weber, B., Rinderle, S., Wild, W., Reichert, M.: Case-Based Reasoning Research and Development. In: 6th International Conference on Case-Based Reasoning, ICCBR 2005, Chicago, IL, USA, August 23–26, 2005. Proceedings, chap. CCBR–Driven Business Process Evolution, pp. 610–624. Springer, Berlin (2005)Google Scholar
  48. 48.
    Weidlich, M., Ziekow, H., Gal, A., Mendling, J., Weske, M.: Optimising event pattern matching using business process models. IEEE Trans. Knowl. Data Eng. 26(11), 2759–2773 (2014)Google Scholar
  49. 49.
    White, S.A.: BPMN Modeling and Reference Guide: Understanding and Using BPMN. Future Strategies Inc., New York (2008)Google Scholar
  50. 50.
    Wieland, M., Schwarz, H., Breitenbucher, U., Leymann, F.: Towards situation-aware adaptive workflows: SitOPT—a general purpose situation-aware workflow management system. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 32–37. IEEE (2015)Google Scholar
  51. 51.
    Wombacher, A.: A-posteriori detection of sensor infrastructure errors in correlated sensor data and business workflows. In: Proceedings of the 9th International Conference on Business Process Management (BPM’11), pp. 329–344. Springer, Heidelberg (2011)Google Scholar
  52. 52.
    Wombacher, A.: How physical objects and business workflows can be correlated. In: Proceedings of the 2011 IEEE International Conference on Services Computing (SCC 2011), pp. 226–233 (2011)Google Scholar
  53. 53.
    Yousfi, A., Bauer, C., Saidi, R., Dey, A.K.: uBPMN: A BPMN extension for modeling ubiquitous business processes. Inf. Softw. Technol. 74, 55–68 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Ronny Seiger
    • 1
    Email author
  • Steffen Huber
    • 1
  • Peter Heisig
    • 1
  • Uwe Aßmann
    • 1
  1. 1.Institute of Software and Multimedia TechnologyTechnische Universität DresdenDresdenGermany

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