PRISM – A Predictive Risk Monitoring Approach for Business Processes

  • Raffaele Conforti
  • Sven Fink
  • Jonas Manderscheid
  • Maximilian Röglinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9850)

Abstract

Nowadays, organizations face severe operational risks when executing their business processes. Some reasons are the ever more complex and dynamic business environment as well as the organic nature of business processes. Taking a risk perspective on the business process management (BPM) lifecycle has thus been recognized as an essential research stream. Despite profound knowledge on risk-aware BPM with a focus on process design, existing approaches for real-time risk monitoring treat instances as isolated when detecting risks. They do not propagate risk information to other instances in order to support early risk detection. To address this gap, we propose an approach for predictive risk monitoring (PRISM). This approach automatically propagates risk information, which has been detected via risk sensors, across similar running instances of the same process in real-time. We demonstrate PRISM’s capability of predictive risk monitoring by applying it in the context of a real-world scenario.

Keywords

Business process management Risk-aware BPM Risk propagation Predictive risk monitoring 

Notes

Acknowledgements

This research is partially funded by the ARC Discovery Project DP150103356 and was partially carried out in the context of the Project Group Business and Information Systems Engineering of the Fraunhofer Institute for Applied Information Technology FIT.

References

  1. 1.
    van der Aalst, W.M.P.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013, 1–37 (2013)CrossRefGoogle Scholar
  2. 2.
    Beverungen, D.: Exploring the interplay of the design and emergence of business processes as organizational routines. Bus. Inf. Syst. Eng. 6, 191–202 (2014)CrossRefGoogle Scholar
  3. 3.
    zur Muehlen, M., Rosemann, M.: Integrating risks in business process models. In: 16th Australasian Conference on Information Systems, pp. 62–72. Association of Information Systems (2005)Google Scholar
  4. 4.
    Basel Committee on Banking Supervision: Basel II: International Convergence of Capital Measurement and Capital Standards (2006)Google Scholar
  5. 5.
    Oxley, M.G., Sarbanes, P.: Sarbanes Oxley Act of 2002, 745–810 (2002)Google Scholar
  6. 6.
    Mock, R., Corvo, M.: Risk analysis of information systems by event process chains. Int. J. Crit. Infrastruct. 1, 247–257 (2005)CrossRefGoogle Scholar
  7. 7.
    Betz, S., Hickl, S., Oberweis, A.: Risk-aware business process modeling and simulation using XML nets. In: 13th Conference on Commerce and Enterprise Computing, pp. 349–356. IEEE (2011)Google Scholar
  8. 8.
    Suriadi, S., Weiß, B., Winkelmann, A., ter Hofstede, A.H.M., Adams, M., Conforti, R., Fidge, C.J., La Rosa, M., Ouyang, C., Pika, A., Rosemann, M., Wynn, M.: Current research in risk-aware business process management - overview, comparison, and gap analysis. Commun. Assoc. Inf. Syst. 34, 933–984 (2014)Google Scholar
  9. 9.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Bolsinger, M.: Bringing value-based business process management to the operational process level. Inf. Syst. E-bus. Manag. 13, 355–398 (2015)CrossRefGoogle Scholar
  11. 11.
    Buhl, H.U., Röglinger, M., Stöckl, S., Braunwarth, K.S.: Value orientation in process management. Bus. Inf. Syst. Eng. 3, 163–172 (2011)CrossRefGoogle Scholar
  12. 12.
    Recker, J., Mendling, J.: The state of the art of business process management research as published in the BPM conference. Bus. Inf. Syst. Eng. 58, 55–72 (2016)CrossRefGoogle Scholar
  13. 13.
    Conforti, R., La Rosa, M., Fortino, G., ter Hofstede, A.H.M., Recker, J., Adams, M.: Real-time risk monitoring in business processes: a sensor-based approach. J. Syst. Softw. 86, 2939–2965 (2013)CrossRefGoogle Scholar
  14. 14.
    Manderscheid, J., Reißner, D., Röglinger, M.: Inspection coming due! How to determine the service interval of your processes! In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 19–34. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  15. 15.
    Conforti, R., ter Hofstede, A.H., La Rosa, M., Adams, M.: Automated risk mitigation in business processes. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 212–231. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)CrossRefGoogle Scholar
  17. 17.
    Krumeich, J., Werth, D., Loos, P.: Prescriptive control of business processes. Bus. Inf. Syst. Eng. 7, 1–40 (2015)Google Scholar
  18. 18.
    Association Information Systems Audit and Control: COBIT 5: A Business Framework for the Governance and Management of Enterprise IT (2013)Google Scholar
  19. 19.
    AXELOS: Information Technology Infrastructure Library. https://www.axelos.com/best-practice-solutions/itil
  20. 20.
    Jakoubi, S., Goluch, G., Tjoa, S., Quirchmayr, G.: Deriving resource requirements applying risk-aware business process modeling and simulation. In: 16th European Conference on Information Systems, pp. 1542–1554. AIS (2008)Google Scholar
  21. 21.
    Sienou, A., Karduck, A.P., Lamine, E., Pingaud, H.: Business process and risk models enrichment: considerations for business intelligence. In: 2008 IEEE International Conference on e-Business Engineering, pp. 732–735. IEEE (2008)Google Scholar
  22. 22.
    Singh, P., Gelgi, F., Davulcu, H., Yau, S.S., Mukhopadhyay, S.: A risk reduction framework for dynamic workflows. In: 2008 IEEE International Conference on Services Computing, pp. 381–388. IEEE (2008)Google Scholar
  23. 23.
    Rotaru, K., Wilkin, C., Churilov, L., Neiger, D., Ceglowski, A.: Formalizing process-based risk with value-focused process engineering. Inf. Syst. E-Bus. Manag. 9, 447–474 (2011)CrossRefGoogle Scholar
  24. 24.
    Karagiannis, D., Mylopoulos, J., Schwab, M.: Business process-based regulation compliance: the case of the Sarbanes-Oxley Act. In: 15th IEEE International Requirements Engineering Conference, pp. 315–321. IEEE (2007)Google Scholar
  25. 25.
    Lambert, J.H., Jennings, R.K., Joshi, N.N.: Integration of risk identification with business process models. Syst. Eng. 9, 187–198 (2006)CrossRefGoogle Scholar
  26. 26.
    Pika, A., van der Aalst, W.M., Fidge, C.J., ter Hofstede, A.H., Wynn, M.T.: Predicting deadline transgressions using event logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 211–216. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  27. 27.
    Suriadi, S., Ouyang, C., van der Aalst, W.M., ter Hofstede, A.H.: Root cause analysis with enriched process logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 174–186. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  28. 28.
    Standards Australia and Standards New Zealand: ISO 31000:2009, Risk Management — Principles and Guidelines (2009)Google Scholar
  29. 29.
    van Dongen, B.F., Dijkman, R., Mendling, J.: Measuring similarity between business process models. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 450–464. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Armas-Cervantes, A., Baldan, P., Dumas, M., García-Bañuelos, L.: Diagnosing behavioral differences between business process models: an approach based on event structures. Inf. Syst. 56, 304–325 (2016)CrossRefGoogle Scholar
  31. 31.
    Polyvyanyy, A., Weidlich, M., Weske, M.: Isotactics as a foundation for alignment and abstraction of behavioral models. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 335–351. Springer, Heidelberg (2012)Google Scholar
  32. 32.
    Dijkman, R.M., Dumas, M., van Dongen, B.F., Käärik, R., Mendling, J.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36, 498–516 (2011)CrossRefGoogle Scholar
  33. 33.
    Beheshti, S.-M.-R., Benatallah, B., Sakr, S., Grigori, D., Motahari-Nezhad, H.R., Barukh, M.C., Gater, A., Ryu, S.H.: Process Analytics - Concepts and Techniques for Querying and Analyzing Process Data. Springer International Publishing, Switzerland (2016)Google Scholar
  34. 34.
    Song, M., Günther, C.W., van der Aalst, W.M.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) Business Process Management Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  35. 35.
    van Beest, N.R.T.P., Dumas, M., García-Bañuelos, L., La Rosa, M.: Log delta analysis: interpretable differencing of business process event logs. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 386–405. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  36. 36.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29, 147–160 (1950)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Jaccard, P.: The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912)CrossRefGoogle Scholar
  38. 38.
    Daniel, F., Eriksson, J., Finne, N., Fuchs, H., Karnouskos, S., Montero, P.M., Mottola, L., Oertel, N., Oppermann, F.J., Picco, G. Pietro, Römer, K., Spieß, P., Tranquillini, S., Voigt, T.: makeSense: real-world business processes through wireless sensor networks. In: 4th International Workshop on Networks of Cooperating Objects for Smart Cities, CONET/UBICITEC, pp. 58–72 (2013)Google Scholar
  39. 39.
    Minor, M., Bergmann, R., Görg, S.: Case-based adaptation of workflows. Inf. Syst. 40, 142–152 (2014)CrossRefGoogle Scholar
  40. 40.
    Motahari-Nezhad, H.R., Bartolini, C.: Next best step and expert recommendation for collaborative processes in IT service management. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 50–61. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  41. 41.
    Stutz, P., Bernstein, A., Cohen, W.: Signal/collect: graph algorithms for the (semantic) web. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 764–780. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  42. 42.
  43. 43.
    Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: Filtering out Infrequent Behavior from Process Event Logs (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Raffaele Conforti
    • 1
  • Sven Fink
    • 2
  • Jonas Manderscheid
    • 2
  • Maximilian Röglinger
    • 3
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.FIM Research CenterUniversity of AugsburgAugsburgGermany
  3. 3.FIM Research CenterUniversity of BayreuthBayreuthGermany

Personalised recommendations