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Planlets: Automatically Recovering Dynamic Processes in YAWL

  • Andrea Marrella
  • Alessandro Russo
  • Massimo Mecella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7565)

Abstract

Process Management Systems (PMSs) are currently more and more used as a supporting tool to coordinate the enactment of processes. YAWL, one of the best-known PMSs coming from academia, allows to define stable and well-understood processes and provides support for the handling of expected exceptions, which can be anticipated at design time. But in some real world scenarios, the environment may change in unexpected ways so as to prevent a process from being successfully carried out. In order to cope with these anomalous situations, a PMS should automatically recover the process at run-time, by considering the context of the specific case under execution. In this paper, we propose the approach of Planlets, self-contained YAWL specifications with recovery features, based on modeling of pre- and post-conditions of tasks and the use of planning techniques. We show the feasibility of the proposed approach by discussing its deployment on top of YAWL.

Keywords

Process Management Systems YAWL recovery planning 

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References

  1. 1.
    de Leoni, M., Mecella, M., De Giacomo, G.: Highly Dynamic Adaptation in Process Management Systems Through Execution Monitoring. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 182–197. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Edelkamp, S., Hoffmann, J.: PDDL2.2: The Language for the Classical Part of the 4th International Planning Competition. Tech. rep., Albert-Ludwigs-Universitat Freiburg, Institut fur Informatik (2004)Google Scholar
  3. 3.
    Ferreira, H., Ferreira, D.: An integrated life cycle for workflow management based on learning and planning. Int. J. Coop. Inf. Syst. 15, 485–505 (2006)CrossRefGoogle Scholar
  4. 4.
    Friedrich, G., Fugini, M., Mussi, E., Pernici, B., Tagni, G.: Exception handling for repair in service-based processes. IEEE Trans. on Soft. Eng. 36, 198–215 (2010)CrossRefGoogle Scholar
  5. 5.
    Gajewski, M., Meyer, H., Momotko, M., Schuschel, H., Weske, M.: Dynamic failure recovery of generated workflows. In: DEXA 2005 (2005)Google Scholar
  6. 6.
    Gerevini, A., Saetti, A., Serina, I.: Planning through stochastic local search and temporal action graphs in Lpg. J. Art. Int. Res. 20(1), 239–290 (2003)zbMATHGoogle Scholar
  7. 7.
    Gerevini, A., Saetti, A., Serina, I., Toninelli, P.: Lpg-td: a fully automated planner for PDDL2.2 domains. In: ICAPS 2004 (2004)Google Scholar
  8. 8.
    Helmert, M.: Complexity results for standard benchmark domains in planning. Art. Int. 143, 219–262 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Jarvis, P., Moore, J., Stader, J., Macintosh, A., du Mont, A.C., Chung, P.: Exploiting AI technologies to realise adaptive workflow systems. In: AAAI Workshop on Agent-Based Systems in the Business Context (1999)Google Scholar
  10. 10.
    Lenz, R., Reichert, M.: IT support for healthcare processes. Premises, challenges, perspectives. Data Knowl. Eng. 61, 39–58 (2007)CrossRefGoogle Scholar
  11. 11.
    Marrella, A., Mecella, M., Russo, A.: Featuring automatic adaptivity through workflow enactment and planning. In: CollaborateCom 2011 (2011)Google Scholar
  12. 12.
    Marrella, A., Mecella, M., Russo, A., ter Hofstede, A.H.M., Sardiña, S.: Making YAWL and SmartPM interoperate: Managing highly dynamic processes by exploiting automatic adaptation features. In: BPM, Demos (2011)Google Scholar
  13. 13.
    R-Moreno, M.D., Borrajo, D., Cesta, A., Oddi, A.: Integrating planning and scheduling in workflow domains. Exp. Syst. with Applications 33(2) (2007)Google Scholar
  14. 14.
    Russell, N., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Workflow Exception Patterns. In: Martinez, F.H., Pohl, K. (eds.) CAiSE 2006. LNCS, vol. 4001, pp. 288–302. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Schonenberg, H., Mans, R., Russell, N., Mulyar, N., van der Aalst, W.M.P.: Process flexibility: A survey of contemporary approaches. In: CIAO! / EOMAS 2008 (2008)Google Scholar
  16. 16.
    Selman, B., Kautz, H.A., Cohen, B.: Noise strategies for improving local search. In: AAAI 1994 (1994)Google Scholar
  17. 17.
    ter Hofstede, A.H.M., van der Aalst, W.M.P., Adams, M., Russell, N.: Modern business process automation: YAWL and its support environment. Springer (2009)Google Scholar
  18. 18.
    Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features - enhancing flexibility in process-aware information systems. Data Knowl. Eng. 66, 438–466 (2008)CrossRefGoogle Scholar
  19. 19.
    Weber, B., Wild, W., Lauer, M., Reichert, M.: Improving exception handling by discovering change dependencies in adaptive process management systems. In: BPI 2006 (2006)Google Scholar
  20. 20.
    Weske, M.: Formal foundation and conceptual design of dynamic adaptations in a workflow management system. In: HICSS 2001 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrea Marrella
    • 1
  • Alessandro Russo
    • 1
  • Massimo Mecella
    • 1
  1. 1.Dipartimento di Ingegneria Informatica, Automatica e GestionaleSapienza Università di RomaRomeItaly

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