Data–Driven Process Control and Exception Handling in Process Management Systems

  • Stefanie Rinderle
  • Manfred Reichert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4001)


Business processes are often characterized by high variability and dynamics, which cannot be always captured in contemporary process management systems (PMS). Adaptive PMS have emerged in recent years, but do not completely solve this problem. In particular, users are not adequately supported in dealing with real–world exceptions. Exception handling usually requires manual interactions and necessary process adaptations have to be defined at the control flow level. Altogether, only experienced users are able to cope with these tasks. As an alternative, changes on process data (elements) can be more easily accomplished, and a more data–driven view on (adaptive) PMS can help to bridge the gap between real–world processes and computerized ones. In this paper we present an approach for data–driven process control allowing for the automated expansion and adaptation of task nets during runtime. By integrating and exploiting context information this approach further enables automated exception handling at a high level and in a user–friendly way. Altogether, the presented work provides an added value to current adaptive PMS.


Context Information Business Process Management Context Data Process Instance Change Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dumas, M., van der Aalst, W., ter Hofstede, A.: Process–Aware Information systems. Wiley, Chichester (2005)Google Scholar
  2. 2.
    Reichert, M., Dadam, P.: ADEPTflex - supporting dynamic changes of workflows without losing control. JIIS 10, 93–129 (1998)Google Scholar
  3. 3.
    Müller, R.: Event-Oriented Dynamic Adaptation of Workflows. PhD thesis, University of Leipzig, Germany (2002)Google Scholar
  4. 4.
    Berry, P., Myers, K.: Adaptive process management: An al perspective. In: Proc. Workshop Towards Adaptive Workflow Systems (CSCW 1998), Seattle (1998)Google Scholar
  5. 5.
    Herrmann, T., Just-Hahn, K.: Organizational learning with flexible workflow management systems. In: WS on Organizational Learning, CSCW 1996, pp. 54–57 (1996)Google Scholar
  6. 6.
    Rinderle, S., Reichert, M., Dadam, P.: Flexible support of team processes by adaptive workflow systems. Distributed and Parallel Databases 16, 91–116 (2004)CrossRefGoogle Scholar
  7. 7.
    Rinderle, S., Reichert, M., Dadam, P.: Correctness criteria for dynamic changes in workflow systems – a survey. DKE 50, 9–34 (2004)CrossRefGoogle Scholar
  8. 8.
    Weske, M.: Formal foundation and conceptual design of dynamic adaptations in a workflow management system. In: HICSS-34 (2001)Google Scholar
  9. 9.
    Elmagarmid, A.: Database Transaction Models for Advanced Applications. Morgan Kaufman, San Francisco (1992)Google Scholar
  10. 10.
    Schuldt, H., Alonso, G., Beeri, C., Schek, H.: Atomicity and isolation for transactional processes. TODS 27, 63–116 (2002)CrossRefGoogle Scholar
  11. 11.
    Leymann, F., Roller, D.: Production Workflow. Prentice Hall, Englewood Cliffs (2000)MATHGoogle Scholar
  12. 12.
    Reichert, M., Rinderle, S., Kreher, U., Dadam, P.: Adaptive process management with adept2. In: ICDE 2005, pp. 1113–1114 (2005)Google Scholar
  13. 13.
    Steggles, P., Cadman, J.: White paper: a comparison of RF tag location products for real-world applications (2004)Google Scholar
  14. 14.
    van der Aalst, W., ter Hofstede, A., Kiepuszewski, B., Barros, A.: Workflow patterns. DPD 14, 5–51 (2003)Google Scholar
  15. 15.
    Guabtni, A., Charoy, F.: Multiple instantiation in a dynamic workflow environment. In: Persson, A., Stirna, J. (eds.) CAiSE 2004. LNCS, vol. 3084, pp. 175–188. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    van der Aalst, W., Weske, M., Grünbauer, D.: Case handling: A new paradigm for business process support. DKE 53, 129–162 (2004)Google Scholar
  17. 17.
    van der Aalst, W.: On the automatic generation of workflow processes based on product structures. Computer in Industry 39, 97–111 (1999)CrossRefGoogle Scholar
  18. 18.
    Müller, R., Greiner, U., Rahm, E.: AgentWork: A workflow-system supporting rule-based workflow adaptation. DKE 51, 223–256 (2004)CrossRefGoogle Scholar
  19. 19.
    Heimann, P., Joeris, G., Krapp, C., Westfechtel, B.: DYNAMITE: Dynamic task nets for software process management. In: ICSE 1996, Berlin, pp. 331–341 (1996)Google Scholar
  20. 20.
    Liu, C., Conradi, R.: Automatic replanning of task networks for process model evolution. In: Sommerville, I., Paul, M. (eds.) ESEC 1993. LNCS, vol. 717, pp. 434–450. Springer, Heidelberg (1993)Google Scholar
  21. 21.
    Wilkins, D., Myers, K., Lowrance, J., Wesley, L.: Planning and reacting in uncertain and dynamic environments. Experimental and Theoret. AI 7, 197–227 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefanie Rinderle
    • 1
  • Manfred Reichert
    • 2
  1. 1.Dept. DBISUniversity of UlmGermany
  2. 2.IS GroupUniversity of TwenteThe Netherlands

Personalised recommendations