Advertisement

Process Mining and Verification of Properties: An Approach Based on Temporal Logic

  • W. M. P. van der Aalst
  • H. T. de Beer
  • B. F. van Dongen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3760)

Abstract

Information systems are facing conflicting requirements. On the one hand, systems need to be adaptive and self-managing to deal with rapidly changing circumstances. On the other hand, legislation such as the Sarbanes-Oxley Act, is putting increasing demands on monitoring activities and processes. As processes and systems become more flexible, both the need for, and the complexity of monitoring increases. Our earlier work on process mining has primarily focused on process discovery, i.e., automatically constructing models describing knowledge extracted from event logs. In this paper, we focus on a different problem complementing process discovery. Given an event log and some property, we want to verify whether the property holds. For this purpose we have developed a new language based on Linear Temporal Logic (LTL) and we combine this with a standard XML format to store event logs. Given an event log and an LTL property, our LTL Checker verifies whether the observed behavior matches the (un)expected/(un)desirable behavior.

Keywords

Process mining temporal logic business process management workflow management data mining Petri nets 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van der Aalst, W.M.P.: Exterminating the Dynamic Change Bug: A Concrete Approach to Support Workflow Change. Information Systems Frontiers 3(3), 297–317 (2001)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Business Alignment: Using Process Mining as a Tool for Delta Analysis. In: Grundspenkis, J., Kirikova, M. (eds.) Proceedings of the 5th Workshop on Business Process Modeling, Development and Support (BPMDS 2004), Riga Technical University, Latvia. Caise 2004 Workshops, vol. 2, pp. 138–145 (2004)Google Scholar
  3. 3.
    van der Aalst, W.M.P., van Hee, K.M.: Workflow Management: Models, Methods, and Systems. MIT press, Cambridge (2002)Google Scholar
  4. 4.
    van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Genetic Process Mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    van der Aalst, W.M.P., de Medeiros, A.K.A.: Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance. In: Busi, N., Gorrieri, R., Martinelli, F. (eds.) Second International Workshop on Security Issues with Petri Nets and other Computational Models (WISP 2004), STAR, Servizio Tipografico Area della Ricerca, CNR Pisa, Italy, pp. 69–84 (2004)Google Scholar
  6. 6.
    van der Aalst, W.M.P., Song, M.: Mining Social Networks: Uncovering Interaction Patterns in Business Processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  8. 8.
    van der Aalst, W.M.P., Weijters, A.J.M.M. (eds.): Process Mining, Special Issue of Computers in Industry, vol. 53(3). Elsevier Science Publishers, Amsterdam (2004)Google Scholar
  9. 9.
    van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  10. 10.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    de Beer, H.: The LTL Checker Plugins: A Reference Manual, Eindhoven University of Technology, Eindhoven (2004)Google Scholar
  12. 12.
    Cook, J.E., He, C., Ma, C.: Measuring Behavioral Correspondence to a Timed Concurrent Model. In: Proceedings of the 2001 International Conference on Software Mainenance, pp. 332–341 (2001)Google Scholar
  13. 13.
    Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)CrossRefGoogle Scholar
  14. 14.
    Cook, J.E., Wolf, A.L.: Software Process Validation: Quantitatively Measuring the Correspondence of a Process to a Model. ACM Transactions on Software Engineering and Methodology 8(2), 147–176 (1999)CrossRefGoogle Scholar
  15. 15.
    Ellis, C.A., Keddara, K., Rozenberg, G.: Dynamic change within workflow systems. In: Comstock, N., Ellis, C., Kling, R., Mylopoulos, J., Kaplan, S. (eds.) Proceedings of the Conference on Organizational Computing Systems, Milpitas, California, August 1995. ACM SIGOIS, pp. 10–21. ACM Press, New York (1995)CrossRefGoogle Scholar
  16. 16.
    Fickas, S., Beauchamp, T., Mamy, N.A.R.: Monitoring Requirements: A Case Study. In: Proceedings of the 17th IEEE International Conference on Automated Software Engineering (ASE 2002), p. 299. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  17. 17.
    Giannakopoulou, D., Havelund, K.: Automata-Based Verification of Temporal Properties on Running Programs. In: Proceedings of the 16th IEEE International Conference on Automated Software Engineering (ASE 2001), pp. 412–416. IEEE Computer Society Press, Providence (2001)CrossRefGoogle Scholar
  18. 18.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.C.: Business process intelligence. Computers in Industry 53(3), 321–343 (2004)CrossRefGoogle Scholar
  19. 19.
    Grigori, D., Casati, F., Dayal, U., Shan, M.C.: Improving Business Process Quality through Exception Understanding, Prediction, and Prevention. In: Apers, P., Atzeni, P., Ceri, S., Paraboschi, S., Ramamohanarao, K., Snodgrass, R. (eds.) Proceedings of 27th International Conference on Very Large Data Bases (VLDB 2001), pp. 159–168. Morgan Kaufmann, San Francisco (2001)Google Scholar
  20. 20.
    Havelund, K., Rosu, G.: Monitoring Programs Using Rewriting. In: Proceedings of the 16th IEEE International Conference on Automated Software Engineering (ASE 2001), pp. 135–143. IEEE Computer Society Press, Providence (2001)CrossRefGoogle Scholar
  21. 21.
    Havelund, K., Roşu, G.: Synthesizing Monitors for Safety Properties. In: Katoen, J.-P., Stevens, P. (eds.) TACAS 2002. LNCS, vol. 2280, pp. 342–356. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Herbst, J.: A Machine Learning Approach to Workflow Management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  23. 23.
    Hoffman, T.: Sarbanes-Oxley Sparks Forensics Apps Interest: Vendors Offer Monitoring Tools to Help Identify Incidents of Financial Fraud. ComputerWorld 38, 14 (2004)Google Scholar
  24. 24.
    IDS Scheer. ARIS Process Performance Manager (ARIS PPM): Measure, Analyze and Optimize Your Business Process Performance (whitepaper), IDS Scheer, Saarbruecken, Gemany (2002), http://www.ids-scheer.com
  25. 25.
    Keller, G., Teufel, T.: SAP R/3 Process Oriented Implementation. Addison-Wesley, Reading (1998)Google Scholar
  26. 26.
    Manna, Z., Pnueli, A.: The Temporal Logic of Reactive and Concurrent Systems: Specification. Springer, New York (1991)zbMATHGoogle Scholar
  27. 27.
    zur Mühlen, M., Rosemann, M.: Workflow-based Process Monitoring and Controlling - Technical and Organizational Issues. In: Sprague, R. (ed.) Proceedings of the 33rd Hawaii International Conference on System Science (HICSS-33), pp. 1–10. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  28. 28.
    Pnueli, A.: The Temporal Logic of Programs. In: Proceedings of the 18th IEEE Annual Symposium on the Foundations of Computer Science, pp. 46–57. IEEE Computer Society Press, Providence (1977)Google Scholar
  29. 29.
    Reichert, M., Dadam, P.: ADEPTflex: Supporting Dynamic Changes of Workflow without Loosing Control. Journal of Intelligent Information Systems 10(2), 93–129 (1998)CrossRefGoogle Scholar
  30. 30.
    Rinderle, S., Reichert, M., Dadam, P.: Correctness Criteria For Dynamic Changes in Workflow Systems: A Survey. Data and Knowledge Engineering 50(1), 9–34 (2004)CrossRefGoogle Scholar
  31. 31.
    Robinson, W.N.: Monitoring Software Requirements using Instrumented Code. In: Proceedings of the 35th Annual Hawaii IEEE International Conference on Systems Sciences, p. 276. IEEE Computer Society, Los Alamitos (2002)Google Scholar
  32. 32.
    Robinson, W.N.: Monitoring Web Service Requirements. In: Proceedings of 11th IEEE International Conference on Requirements Engineering (RE 2003), pp. 56–74. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  33. 33.
    Sarbanes, P., Oxley, G., et al.: Sarbanes-Oxley Act of 2002 (2002)Google Scholar
  34. 34.
    Sayal, M., Casati, F., Dayal, U., Shan, M.C.: Business Process Cockpit. In: Proceedings of 28th International Conference on Very Large Data Bases (VLDB 2002), pp. 880–883. Morgan Kaufmann, San Francisco (2002)CrossRefGoogle Scholar
  35. 35.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • W. M. P. van der Aalst
    • 1
  • H. T. de Beer
    • 1
  • B. F. van Dongen
    • 1
  1. 1.Department of Technology ManagementEindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations