Rapid Business Process Discovery (R-BPD)

  • Aditya Ghose
  • George Koliadis
  • Arthur Chueng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4801)


Modeling is an important and time consuming part of the Business Process Management life-cycle. An analyst reviews existing documentation and queries relevant domain experts to construct both mental and concrete models of the domain. To aid this exercise, we propose the Rapid Business Process Discovery (R-BPD) framework and prototype tool that can query heterogeneous information resources (e.g. corporate documentation, web-content, code e.t.c.) and rapidly construct proto-models to be incrementally adjusted to correctness by an analyst. This constitutes a departure from building and constructing models toward just editing them. We believe this rapid mixed-initiative modeling will increase analyst productivity by significant orders of magnitude over traditional approaches. Furthermore, the possibility of using the approach in distributed and real-time settings seems appealing and may help in significantly improving the quality of the models being developed w.r.t. being consistent, complete, and concise.


Model Drive Architecture Business Process Modeling Notation Model Drive Architecture Edge Pair Credit Check 
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.
    Smith, H., Fingar, P.: Business Process Management: The Third Wave. Meghan-Kiffer Press, Tampa, FL (2003)Google Scholar
  2. 2.
    van der Aalst, W.M., ter Hofstede, A.H., Weske, M.: Business process management: A survey. In: van der Aalst, W.M.P., ter Hofstede, A.H.M., Weske, M. (eds.) BPM 2003. LNCS, vol. 2678, pp. 1–12. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Sommerville, I., Sawyer, P., Viller, S.: Manging process inconsistency using viewpoints. IEEE Transactions on Software Engineering 25, 784–799 (1999)CrossRefGoogle Scholar
  4. 4.
    Gruber, T.R.: Automated knowledge acquisition for strategic knowledge. Machine Learning 4, 293–336 (1989)Google Scholar
  5. 5.
    RBPD: Rapid model discovery project,
  6. 6.
    Boose, J.H.: Knowledge aquisition, methods, and mediating representations. In: First Japanese Knowledge Aquisition for Knowledge-Based Systems Workshop (JKAW’90) (1990)Google Scholar
  7. 7.
    Hoffman, R.R., Shadbolt, N.R., Burton, M., Klein, G.: Eliciting knowledge from experts: A methodological analysis. Organizational Behaviour and Human Decision Processes 62, 129–158 (1995)CrossRefGoogle Scholar
  8. 8.
    Young, R.M., Gammack, J.: The role of psychological techniques and intermediate representations in knowledge elicitation. In: Proceedings of the First European Workshop on Knowledge Acquisition and Knowledge-based Systems (1987)Google Scholar
  9. 9.
    Shore, B.: Bias in the development and use of an expert system: Implications for lifecycle costs. Industrial Management and Data Systems 4, 18–26 (1996)CrossRefGoogle Scholar
  10. 10.
    Hoffman, R.R.: Bibliography: Automated knowledge elicitation, representation, and instantiation. In: Bibliography: Automated knowledge elicitation, representation, and instantiation, pp. 346–358. Erlbaum, Hillsdale, NJ (1992)Google Scholar
  11. 11.
    Reubenstein, H.B., Waters, R.C.: The requirements apprentice: Automated assistance for requirements acquisition. IEEE Trans. Softw. Eng. 17(3), 226–240 (1991)CrossRefGoogle Scholar
  12. 12.
    de Medeiros, A., van der Aalst, W., Weijters, A.: Workflow mining: Current status and future directions. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, Springer, Heidelberg (2003)Google Scholar
  13. 13.
    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
  14. 14.
    Rozinat, A.W.M.P.v.d.A.: Decision mining in prom. In: Business Process Management. (2006) 420–425Google Scholar
  15. 15.
    Ellis, C.A., Rembert, A.J., Kim, K.-H., Wainer, J.: Beyond worflow mining. In: Dustdar, S., Fiadeiro, J.L., Sheth, A. (eds.) BPM 2006. LNCS, vol. 4102, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Sommerville, I., Sawyer, P.: Viewpoints: Principles, problems and a practical approach to requirements engineering. Annals of Software Engineering 3, 101–130 (1997)CrossRefGoogle Scholar
  17. 17.
    Easterbrook, S., Nuseibeh, B.: Using viewpoints for inconsistency management. BCS/IEE Software Engineering Journal, 31–43 (January 1996)Google Scholar
  18. 18.
    Easterbrook, S., Finkelstein, A., Kramer, J., Nuseibeh, B.: Co-ordinating distributed viewpoints: the anatomy of a consistency check. Technical Report 94/7, Department of Computing, Imperial College, London (1994)Google Scholar
  19. 19.
    Czarnecki, K., Helsen, S.: Classification of model transformation approaches. In: Proc. OOPSLA’03 Workshop on Generative Techniques in the Context of Model-Driven Architecture (2003)Google Scholar
  20. 20.
    Koliadis, G., Vranesevic, A., Bhuiyan, M., Krishna, A., Ghose, A.: A combined approach for supporting the business process model lifecycle. In: Proc. of the 10th Pacific Asia Conference on Information Systems (PACIS’06) (2006)Google Scholar
  21. 21.
    Holocentric (2007),
  22. 22.
    Xu, K., Lianchen, L., Wu, C.: A three-layered method for business process discovery and its application in manufacturing industry. Computers in Industry 58, 265–278 (2007)CrossRefGoogle Scholar
  23. 23.
    BEA: Introduction to business process management and the sample workflows. Accessed: 27.02.07 (2007),
  24. 24.
    Klein, E.: Computational semantics in the natural language toolkit. In: Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006), pp. 26–33 (2006)Google Scholar
  25. 25.
    Pham, S., Hoffmann, A.: Efficient knowledge acquisition for extracting temporal relations. In: Proceedings of the Australasian language technology workshop, pp. 87–95 (2005)Google Scholar
  26. 26.
    Yu, E.: Models for supporting the redesign of organizational work. In: Proceedings of Conf. on Organizational Computing Systems (COOCS’95), Milpitas, CA: USA, August 13-16 1995, pp. 225–236 (1995)Google Scholar
  27. 27.
    White, S.: Business process modeling notation (bpmn), Technical report, OMG Final Adopted Specification 1.0 (2006),
  28. 28.
    Lu, R., Sadiq, S.: Managing process variants as an information resource. In: Dustdar, S., Fiadeiro, J.L., Sheth, A. (eds.) BPM 2006. LNCS, vol. 4102, Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Aditya Ghose
    • 1
  • George Koliadis
    • 1
  • Arthur Chueng
    • 1
  1. 1.Decision Systems Lab (DSL), School of Computer Science and Software Engineering, University of Wollongong, NSW 2522Australia

Personalised recommendations