History-Aware, Real-Time Risk Detection in Business Processes

  • Raffaele Conforti
  • Giancarlo Fortino
  • Marcello La Rosa
  • Arthur H. M. ter Hofstede
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7044)

Abstract

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run-time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

Keywords

Business Process Sensor Condition Risk Condition Service Level Agreement Process Instance 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Albrecht, W.S., Albrecht, C.C., Albrecht, C.O.: Fraud Examination, 3rd edn. South-Western Publishing (2008)Google Scholar
  2. 2.
    Basel Committee on Bankin Supervision. Basel II - International Convergence of Capital Measurement and Capital Standards (2006)Google Scholar
  3. 3.
    Bhushan, N., Rai, K.: Strategic Decision Making: Applying the Analytic Hierarchy Process, 3rd edn. Springer, Heidelberg (2004)MATHGoogle Scholar
  4. 4.
    International Electrotechnical Commission. IEC 61025 Fault Tree Analysis, FTA (1990)Google Scholar
  5. 5.
    Conforti, R., Fortino, G., La Rosa, M., ter Hofstede, A.H.M.: History-aware, real-time risk detection in business processes (extended version). QUT ePrints 42222, Queensland University of Technology (2011), http://eprints.qut.edu.au/42222
  6. 6.
    Davis, R.B., Brabander, E.: ARIS Design Platform: Getting Started with BPM. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Dumas, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Process-Aware Information Systems: Bridging People and Software through Process Technology. Wiley & Sons (2005)Google Scholar
  8. 8.
    Gambini, M., La Rosa, M., Migliorini, S., ter Hofstede, A.H.M.: Automated Error Correction of Business Process Models. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 148–165. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gay, P., Pla, A., López, B., Meléndez, J., Meunier, R.: Service workflow monitoring through complex event processing. In: ETFA. IEEE (2010)Google Scholar
  10. 10.
    Goluch, G., Tjoa, S., Jakoubi, S., Quirchmayr, G.: Deriving resource requirements applying risk-aware business process modeling and simulation. In: ECIS. AISeL (2008)Google Scholar
  11. 11.
    Hermosillo, G., Seinturier, L., Duchien, L.: Using Complex Event Processing for Dynamic Business Process Adaptation. In: SCC. IEEE (2010)Google Scholar
  12. 12.
    Hespos, R., Strassmann, P.: Stochastic Decision Trees for the Analysis of Investment Decisions. Management Science 11(10) (1965)Google Scholar
  13. 13.
    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, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Hollingsworth, D.: The Workflow Reference Model. Workflow Management Coalition (1995)Google Scholar
  15. 15.
    Jakoubi, S., Tjoa, S.: A reference model for risk-aware business process management. In: CRiSIS. IEEE (2009)Google Scholar
  16. 16.
    Jakoubi, S., Tjoa, S., Goluch, S., Kitzler, G.: Risk-Aware Business Process Management: Establishing the Link Between Business and Security. In: Xhafa, F., et al. (eds.) Complex Intelligent Systems and Their Applications. Optimization and its Applications, vol. 41, pp. 109–135. Springer Science+Business Media, LLC (2010)CrossRefGoogle Scholar
  17. 17.
    Johnson, W.G.: MORT - The Management Oversight and Risk Tree. U.S. Atomic Energy Commission (1973)Google Scholar
  18. 18.
    Little, A., Best, P.: A framework for separation of duties in an sap r/3 environment. Managerial Auditing Journal 18(5), 419–430 (2003)CrossRefGoogle Scholar
  19. 19.
    Meyer, B.: Introduction to the theory of programming languages. Prentice-Hall (1990)Google Scholar
  20. 20.
    Neiger, D., Churilov, L., zur Muehlen, M., Rosemann, M.: Integrating risks in business process models with value focused process engineering. In: ECIS, AISeL (2006)Google Scholar
  21. 21.
    OMG. Business Process Model and Notation (BPMN) ver. 2.0 (January 2011), http://www.omg.org/spec/BPMN/2.0
  22. 22.
    Oracle. BPEL Process Manager Developer’s Guide, http://download.oracle.com/docs/cd/E15523_01/integration.1111/e10224/bp_sensors.htm (accesssed June 2011)
  23. 23.
    Rosemann, M., zur Muehlen, M.: Integrating risks in business process models. In: ACIS. AISeL (2005)Google Scholar
  24. 24.
    Smith, K.I., Everson, R.M., Fieldsend, J.E., Murphy, C., Misra, R.: Dominance-based multiobjective simulated annealing. IEEE Trans. on Evolutionary Computation 12(3) (2008)Google Scholar
  25. 25.
    Soldal Lund, M., Solhaug, B., Stolen, K.: Model-Driven Risk Analysis. Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  26. 26.
    Standards Australia and Standards New Zealand. Standard AS/NZS ISO 31000 (2009)Google Scholar
  27. 27.
    Sybase. Sybase CEP Implementation Methodology for Continuous Intelligence, http://www.sybase.com.au/files/White_Papers/Sybase_CEP_Implementation_Methodology_wp.pdf (accessed June 2011)
  28. 28.
    Tjoa, S., Jakoubi, S., Quirchmayr, G.: Enhancing business impact analysis and risk assessment applying a risk-aware business process modeling and simulation methodology. In: ARES, pp. 179–186. IEEE Computer Society (2008)Google Scholar
  29. 29.
    van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle Time Prediction: When Will This Case Finally Be Finished? In: Chung, S. (ed.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Voluntary Interindustry Commerce Solutions Association. Voluntary Inter-industry Commerce Standard (VICS), http://www.vics.org (accessed June 2011)
  31. 31.
    Wang, D., Rundensteiner, E.A., Ellison, R.T., Wang, H.: Active complex event processing infrastructure: Monitoring and reacting to event streams. In: ICDEW. IEEE (2011)Google Scholar
  32. 32.
    zur Mühlen, M., Ho, D.T.-Y.: Risk Management in the BPM Lifecycle. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 454–466. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Raffaele Conforti
    • 1
  • Giancarlo Fortino
    • 2
  • Marcello La Rosa
    • 1
  • Arthur H. M. ter Hofstede
    • 1
    • 3
  1. 1.Queensland University of TechnologyAustralia
  2. 2.Università della CalabriaItaly
  3. 3.Eindhoven University of TechnologyThe Netherlands

Personalised recommendations