Goal-Driven Business Process Derivation

  • Aditya K. Ghose
  • Nanjangud C. Narendra
  • Karthikeyan Ponnalagu
  • Anurag Panda
  • Atul Gohad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)


Solutions to the problem of deriving business processes from goals are critical in addressing a variety of challenges facing the services and business process management community, and in particular, the challenge of quickly generating large numbers of effective process designs (often a bottleneck in industry-scale deployment of BPM). The problem is similar to the planning problem that has been extensively studied in the artificial intelligence (AI) community. However, the direct application of AI planning techniques places an onerous burden on the analyst, and has proven to be difficult in practice. We propose a practical yet rigorous (semi-automated) algorithm for business process derivation from goals. Our approach relies on being able to decompose process goals to a more refined collection of sub-goals whose ontology is aligned with that of the effects of available tasks which can be used to construct the business process. Once process goals are refined to this level, we are able to generate a process design using a procedure that leverages our earlier work on semantic effect annotation of process designs. We illustrate our ideas throughout this paper with a real-life running example, and also present a proof-of-concept prototype implementation.


business process goals tasks capabilities 


  1. 1.
    Governatori, G., Rotolo, A.: A conceptually rich model of business process compliance. In: APCCM, pp. 3–12 (2010)Google Scholar
  2. 2.
    Governatori, G., Milosevic, Z., Sadiq, S.W.: Compliance checking between business processes and business contracts. In: EDOC, pp. 221–232 (2006)Google Scholar
  3. 3.
    Awad, A., Goré, R., Thomson, J., Weidlich, M.: An Iterative Approach for Business Process Template Synthesis from Compliance Rules. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 406–421. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall (2009)Google Scholar
  5. 5.
    Henneberger, M., Heinrich, B., Lautenbacher, F., Bauer, B.: Semantic-based planning of process models. In: Multikonferenz Wirtschaftsinformatik (2008)Google Scholar
  6. 6.
    Heinrich, B., Bolsinger, M., Bewernik, M.: Automated planning of process models: The construction of exclusive choices. In: ICIS, paper 184 (2009)Google Scholar
  7. 7.
    Mukherjee, S., Davulcu, H., Kifer, M., Senkul, P., Yang, G.: Logic based approaches to workflow modeling and verification (2003)Google Scholar
  8. 8.
    Hinge, K., Ghose, A.K., Koliadis, G.: Process seer: A tool for semantic effect annotation of business process models. In: EDOC, pp. 54–63 (2009)Google Scholar
  9. 9.
    Darimont, R., van Lamsweerde, A.: Formal refinement patterns for goal-driven requirements elaboration. SIGSOFT Software Engineering Notes 21, 179–190 (1996)CrossRefGoogle Scholar
  10. 10.
    Dardenne, A., van Lamsweerde, A., Fickas, S.: Goal-directed requirements acquisition. Sci. Comput. Program. 20(1-2), 3–50 (1993)CrossRefzbMATHGoogle Scholar
  11. 11.
    Ghose, A., Koliadis, G.: Auditing Business Process Compliance. In: Krämer, B.J., Lin, K.-J., Narasimhan, P. (eds.) ICSOC 2007. LNCS, vol. 4749, pp. 169–180. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Carbonell, J., et al.: Context-based machine translation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas, pp. 19–28 (2006)Google Scholar
  13. 13.
    Narendra, N.: A goal-based and risk-based approach to creating adaptive workflow processes. In: AAAI Spring Symposium on Bringing Knowledge to Business Processes (2000)Google Scholar
  14. 14.
    Lautenbacher, F., Bauer, B., Forg, S.: Process mining for semantic business process modeling. In: Enterprise Distributed Object Computing Conference Workshops, EDOCW 2009, September 13, pp. 45–53 (2009)Google Scholar
  15. 15.
    Lautenbacher, F., Eisenbarth, T., Bauer, B.: Process model adaptation using semantic technologies. In: Enterprise Distributed Object Computing Conference Workshops, EDOCW 2009, September 13, pp. 301–309 (2009)Google Scholar
  16. 16.
    Gotz, M., Roser, S., Lautenbacher, F., Bauer, B.: Token analysis of graph-oriented process models. In: Enterprise Distributed Object Computing Conference Workshops, EDOCW 2009, September 13, pp. 15 –24 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aditya K. Ghose
    • 1
  • Nanjangud C. Narendra
    • 2
  • Karthikeyan Ponnalagu
    • 2
  • Anurag Panda
    • 3
  • Atul Gohad
    • 3
  1. 1.University of WollongongWollongongAustralia
  2. 2.IBM Research IndiaBangaloreIndia
  3. 3.IBM India Software LabBangaloreIndia

Personalised recommendations