Business Process Improvement Framework and Representational Support

  • Azeem LodhiEmail author
  • Veit Köppen
  • Gunter Saake
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 179)


Business process management and improvement are vital for enterprises in competitive environments. Understanding of a process is a pre-requisite and important step for improvement. Interaction between humans, computers, and business objects provide excellent opportunities for knowledge extraction. However, the specification of a framework is required for business process improvement, which extends from data collection, analytical methods, storage, and representation of knowledge. The process models conceived for information system development are not sufficient for post execution analysis and improvement. In this paper, we specify such a framework briefly and focus on providing representational support for business process improvement. The main objective is to improve the overall improvement process by providing enriched graphical process models. Furthermore, we use a case study to explain the proposed usage and extensions of an existing modeling language for business process improvement.



Azeem Lodhi is supported by a grant from the federal state of Saxony-Anhalt in Germany. This work is partially supported by the German Ministry of Education and Science (BMBF), within the ViERforES-II project No. 01IM10002B.


  1. 1.
    van der Aalst, W., Weijters, A.: Process mining: A research agenda. Comput. Ind. 53, 231–244 (2004)CrossRefGoogle Scholar
  2. 2.
    Kassem, G., Rautenstrauch, C.: Application usage mining to improve enterprise workflows: ERP systems SAP R/3 as example. In: Khosrow-Pour, M. (eds.) Managing Modern Organizations Through Information Technology. Information Resources Management Association pp. 358–362. (2005)Google Scholar
  3. 3.
    Lodhi, A., Köppen, V., Saake, G.: Business process modeling: Active research areas and challenges. Technical Report 1, University of Magdeburg, Germany (2011)Google Scholar
  4. 4.
    Vergidis, K., Tiwari, A., Majeed, B.: Business process analysis and optimization: Beyond reengineering. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(1), 69–82 (2008)CrossRefGoogle Scholar
  5. 5.
    Lodhi, A., Köppen, V., Saake, G.: Post execution analysis of business processes: Taxonomy and challenges. Technical Report 9, University of Magdeburg, Germany (2010)Google Scholar
  6. 6., OMG: Business Process Modeling Notation Specification, Final Adopted Specification (2006)Google Scholar
  7. 7.
    Khan, A., Lodhi, A., Köppen, V., Kassem, G., Saake, G.: Applying process mining in SOA environments. In: Dan, A., Gittler, F., Toumani, F. (eds.) Service-Oriented Computing ICSOC Service Wave 2009 Workshops. Volume 6275 of Lecture Notes in Computer Science. pp. 293–302. Springer, New York (2010)Google Scholar
  8. 8.
    Ingvaldsen, J., Gulla, J.: Preprocessing support for large scale process mining of SAP transactions. Business Process Manag. Workshops 4928, 30–41 (2008)CrossRefGoogle Scholar
  9. 9.
    van Dongen, B., van der Aalst, W.: A meta model for process mining data. In: Missikoff, M., Nicola, A.D. (eds.) Proceedings of the Open Interop Workshop on Enterprise Modelling and Ontologies for Interoperability, Co-located with CAiSE. Volume 160 of CEUR Workshop Proceedings. Porto, Portugal (2005). CEUR-WS.orgGoogle Scholar
  10. 10.
    Günther, C., van der Aalst, W.: A generic import framework for process event logs. In: Eder, J., Dustdar, S. (eds.) Business Process Management Workshops, Workshop on Business Process Intelligence. vol. 4103, pp. 81–92. Springer, Berlin (2006)Google Scholar
  11. 11.
    zur Muehlen, M.: Business process analytics format (BPAF) Document Number WFMC-TC-1015. Workflow Management Coalition, USA (2008)Google Scholar
  12. 12.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.C.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)CrossRefGoogle Scholar
  13. 13.
    Rozinat, A., van der Aalst, W.: Decision mining in ProM. In: International Conference on Business Process Management (BPM 2006), vol. 4102, pp. 420–425. Springer, Berlin (2006)Google Scholar
  14. 14.
    van der Aalst, W.: Business alignment: Using process mining as a tool for delta analysis and conformance testing. Requirement Eng. 10(3), 198–211 (2005)CrossRefGoogle Scholar
  15. 15.
    van der Aalst, W., Reijers, H.A., Song, M.: Discovering social networks from event logs. Comput. Support. Coop. Work 14(6), 549–593 (2005)CrossRefGoogle Scholar
  16. 16.
    Lodhi, A., Kassem, G., Köppen, V., Saake, G.: Investigation of graph mining for business processes. In: Proceedings of The International Conference on Intelligence and Information Technology ICIIT. vol. 2, pp. 293–297. IEEE Computer Society Lahore, Pakistan (2010)Google Scholar
  17. 17.
    Cumberlidge, M.: Business Process Management with JBoss jBPM: A Practical Guide for Business Analysts. Packt Publishing, Birmingham, UK (2007)Google Scholar
  18. 18.
    Scheer, A.W.: ARIS-Business Process Modeling. 2 edn. Springer, New York (1998)Google Scholar
  19. 19.
    IBM: Flowcharting techniques. Technical report, IBM Data Processing Techniques. Yorktown Heights, New York (1969)Google Scholar
  20. 20.
    Booch, G., Rumbaugh, J., Jacobson, I.: Unified Modeling Language User Guide. 2 edn. Addison-Wesley, Boston, MA (2005)Google Scholar
  21. 21.
    Vullers, M.J., Kleingeld, P., Loosschilder, M., Reijers, H.A.: Performance measures to evaluate the impact of best practices. In: Proceedings of Workshops and Doctoral Consortium of the 19th International Conference on Advanced Information Systems Engineering (BPMDS), pp. 359–368. Tapir Academic Press, Trondheim (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Technical and Business Information Systems, Faculty of Computer ScienceUniversity of MagdeburgMagdeburgGermany

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