Business Artifact-Centric Modeling for Real-Time Performance Monitoring

  • Rong Liu
  • Roman Vaculín
  • Zhe Shan
  • Anil Nigam
  • Frederick Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6896)


In activity-centric process paradigm, developing effective and efficient performance models is a hard and laborious problem with many challenges mainly because of the fragmented nature of this paradigm. In this paper, we propose a novel approach to performance monitoring based on business artifact-centric process paradigm. Business artifacts provide an appropriate base for explicit modeling of monitoring contexts. We develop a model-driven two-phase methodology for designing real-time monitoring models. This methodology allows domain experts or business users to focus on defining metric and KPI requirements while the detailed technical specification of monitoring models can be automatically generated from the requirements and underlying business artifacts. This approach dramatically simplifies design of monitoring models and also increases the understandability of monitoring results.


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  1. 1.
    Zisman, M.: Ibm’s vision of the on demand enterprise. In: IBM Almaden Conference on Co-Evolution of Business and Technology (2003)Google Scholar
  2. 2.
    White, C.: Now is the right time for real-time bi. Information Management Magazine (2004)Google Scholar
  3. 3.
    Costello, C., Molloy, O.: Building a process performance model for business activity monitoring. Information Systems Development, 237–248 (2009)Google Scholar
  4. 4.
    Jeng, J.J., Chang, H., Bhaskaran, K.: Policy driven business performance management. In: Sahai, A., Wu, F. (eds.) DSOM 2004. LNCS, vol. 3278, pp. 52–63. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Thomas, M., Redmond, R., Yoon, V., Singh, R.: A semantic approach to monitor business process. Commun. ACM 48, 55–59 (2005)CrossRefGoogle Scholar
  6. 6.
    Lakshmanan, G.T., Keyser, P., Slominski, A., Curbera, F., Khalaf, R.: A business centric end-to-end monitoring approach for service composites. In: 2010 IEEE International Conference on Services Computing (SCC 2010), pp. 409–416. IEEE Computer Society, Los Alamitos (2010)CrossRefGoogle Scholar
  7. 7.
    Corea, S., Watters, A.: Challenges in business performance measurement: The case of a corporate it function. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 16–31. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Luckham, D.C.: The power of events: an introduction to complex event processing in distributed enterprise systems. Addison-Wesley, Boston (2002)Google Scholar
  9. 9.
    Gassman, B.: How the pieces in a BAM architecture work. Technical report, Gartner Research (2004)Google Scholar
  10. 10.
    Nesamoney, D.: Bam: Event-driven business intelligence for the real-time enterprise. Information Management Magazine 14, 38–48 (2004)Google Scholar
  11. 11.
    White, S.A.: Introduction to BPMN. IBM Cooperation (May 2004)Google Scholar
  12. 12.
    Nigam, A., Caswell, N.S.: Business artifacts: An approach to operational specification. IBM Systems Journal 42, 428–445 (2003)CrossRefGoogle Scholar
  13. 13.
    Bhattacharya, K., Caswell, N.S., Kumaran, S., Nigam, A., Wu, F.Y.: Artifact-centered operational modeling: Lessons from customer engagements. IBM Systems Journal 46, 703–721 (2007)CrossRefGoogle Scholar
  14. 14.
    Chao, T., Cohn, D., Flatgard, A., Hahn, S., Linehan, M.H., Nandi, P., Nigam, A., Pinel, F., Vergo, J., Wu, F.Y.: Artifact-based transformation of IBM global financing. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 261–277. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Sill, B.: Brand metrics can help restaurant service measure up to customer expectations. Nation’s Restaurant News, 24 (May 27, 2002)Google Scholar
  16. 16.
  17. 17.
    IBM: Common Event Infrastructure (2004),
  18. 18.
    Cohn, D., Dhoolia, P., Heath, F., Pinel, F., Vergo, J.: Siena: From power point to web app in 5 minutes. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 722–723. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    White, S.: Best practices for using websphere business modeler and monitor. Technical report, IBM (2006)Google Scholar
  20. 20.
    Ballard, C., White, C., McDonald, S., Myllymaki, J., McDowell, S., Goerlich, O., Neroda, A.: Business performance management... meets business intelligence (2005),
  21. 21.
    Kaplan, R.S., Norton, D.P.: The balanced scorecard - measures that drive performance. Harvard Business Review 70, 71–79 (1992)Google Scholar
  22. 22.
    Folan, P., Browne, J.: A review of performance measurement: towards performance management. Computers in Industry 56, 663–680 (2005)CrossRefGoogle Scholar
  23. 23.
    Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M.: Designing performance measures: a structured approach. International Journal of Operations & Production Management 17, 1131–1152 (1997)CrossRefGoogle Scholar
  24. 24.
    IBM: Ibm cognos now! v4.6 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rong Liu
    • 1
  • Roman Vaculín
    • 1
  • Zhe Shan
    • 2
  • Anil Nigam
    • 1
  • Frederick Wu
    • 1
  1. 1.IBM T.J. Watson Research CenterHawthorneUSA
  2. 2.Department of Supply Chain and Information Systems, Smeal College of BusinessPenn State UniversityUSA

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