In today’s ever changing world, business processes need to be dynamic. Data accumulated as the processes operate capture the meaning of transactions in the past, which opens a door for the dynamics of the business processes in question. Mining the operational data to explicitly represent this meaning could lead to process re-design to make the business processes more efficient. In this paper, we propose a formal framework for redesigning business processes taking data mining rules and business rules as the driver. We formally represent business processes using the artifact-centric approach put forward by the IBM Research. We devise redesigning algorithms that take classification rules extracted from data mining together with business rules and transform the business process in question by eliminating redundant tasks and/or re-ordering inefficiently placed tasks. We illustrate our algorithms and report experiments that were conducted using a proof-of-concept case-study.


Artifact-centric processes Process redesign Process modeling Data mining Formal methods 


  1. 1.
    Wegener, D., Rüping, S.: On integrating data mining into business processes. In: Abramowicz, W., Tolksdorf, R. (eds.) BIS 2010. LNBIP, vol. 47, pp. 183–194. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Niedermann, F., Radeschütz, S., Mitschang, B.: Design-time process optimization through optimization patterns and process model matching. In: Proceedings of the 12th IEEE Conference on Commerce and Enterprise Computing, Shanghai, China, pp. 48–55. IEEE Computer Society (2010)Google Scholar
  3. 3.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–54 (1996)Google Scholar
  4. 4.
    van der Aalst, W.M.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013, 1–37 (2013)CrossRefGoogle Scholar
  5. 5.
    Mansar, S.L., Reijers, H.A.: Best practices in business process redesign: use and impact. bus. process manag. j. 13(2), 193–213 (2007)CrossRefGoogle Scholar
  6. 6.
    Wegener, D., Rüping, S.: On reusing data mining in business processes - a pattern-based approach. In: Muehlen, M., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 264–276. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    van der Aalst, W., van Hee, K.: Business process redesign: a Petri-net-based approach. Comput. Ind. 29(1–2), 15–26 (1996)CrossRefGoogle Scholar
  8. 8.
    Koliadis, G., Ghose, A.: A conceptual framework for business process redesign. In: Halpin, H., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) BPMDS 2009 and EMMSAD 2009. LNBIP, vol. 29, pp. 14–26. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)Google Scholar
  10. 10.
    Rupnik, R., Jaklič, J.: The deployment of data mining into operational business processes. In Ponce, J., Karahoca, A. (eds.) Data Mining and Knowledge Discovery in Real Life Applications. I-Tech Education and Publishing (2009)Google Scholar
  11. 11.
    Yongchareon, S., Liu, C.: A process view framework for artifact-centric business processes. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 26–43. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kunchala, J., Yu, J., Yongchareon, S.: A survey on approaches to modeling artifact-centric business processes. In: Benatallah, B., Bestavros, A., Catania, B., Haller, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 9051, pp. 117–132. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  13. 13.
    Bhattacharya, K., Gerede, C.E., Hull, R., Liu, R., Su, J.: Towards formal analysis of artifact-centric business process models. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 288–304. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Estañol, M., Queralt, A., Sancho, M.R., Teniente, E.: Artifact-centric business process models in UML. In: La Rosa, M., Soffer, P. (eds.) BPM 2012 Workshops. LNBIP, vol. 132, pp. 292–303. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Lohmann, N., Nyolt, M.: Artifact-centric modeling using BPMN. In: Pallis, G., et al. (eds.) ICSOC 2011 Workshops. LNCS, vol. 7221, pp. 54–65. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Hinge, K., Ghose, A., Koliadis, G.: Process SEER: a tool for semantic effect annotation of business process models. In: Proceedings of the 13th IEEE International Conference on Enterprise Distributed Object Computing, Auckland, New Zealand, pp. 49–58. IEEE Computer Society, September 2009Google Scholar
  17. 17.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehousing 5(4), 13–22 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringHo Chi Minh City University of TechnologyHo Chi Minh CityVietnam

Personalised recommendations