Focusing Business Improvements Using Process Mining Based Influence Analysis

  • Teemu LehtoEmail author
  • Markku Hinkka
  • Jaakko Hollmén
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 260)


Business processes are traditionally regarded as generalized abstractions describing the activities and common behaviour of a large group of process instances. However, the recent developments in process mining and data analysis show that individual process instances may behave very different from each other. In this paper we present a generic methodology called influence analysis for finding business improvement areas related to business processes. Influence analysis is based on process mining, root cause analysis and classification rule mining. We present three generic target levels for business improvements and define corresponding probability-based interestingness measures. We then define measures for reporting the contribution results to business people and show how these measures can be used to focus improvements. Real-life case study is also included to show the methodology in action.


Process analysis Process improvement Process mining Classification rule mining Root cause analysis Data mining Influence analysis Contribution 



We thank QPR Software Plc for the practical experiences from a wide variety of customer cases and for funding our research.


  1. 1.
    Andersen, B., Fagerhaug, T.: Root Cause Analysis: Simplified Tools and Techniques. ASQ Quality Press, Milwaukee (2006)Google Scholar
  2. 2.
    Bay, S., Pazzani, M.: Detecting group differences: mining contrast sets. Data Min. Knowl. Disc. 5(3), 213–246 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general framework for correlating business process characteristics. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 250–266. Springer, Heidelberg (2014)Google Scholar
  4. 4.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. (CSUR) 38(3), 9 (2006)Google Scholar
  5. 5.
    Goldratt, E.M.: Theory of Constraints. North River, Croton-on-Hudson (1990)Google Scholar
  6. 6.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Inmon, W.H.: Building the Data Warehouse. Wiley, New York (2005)Google Scholar
  8. 8.
    Kakas, A.C., Kowalski, R.A., Toni, F.: Abductive logic programming. J. Logic Comput. 2(6), 719–770 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Mayer-Schnberger, V., Cukier, K.: Big Data: A Revolution that Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, Boston (2013)Google Scholar
  10. 10.
    Pearl, J.: Causality: Models, Reasoning and Inference, vol. 29. MIT Press, Cambridge (2000)zbMATHGoogle Scholar
  11. 11.
    Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248 (1991)Google Scholar
  12. 12.
    QPR Software Plc.: QPR Software to Offer Business Process optimization with Automated Business Process Discovery Software QPR Process Analyzer, Press release 15 Feb 2011Google Scholar
  13. 13.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  14. 14.
    Rozinat, A., van der Aalst, W.M.P.: Decision Mining in ProM. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Suriadi, S., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Root cause analysis with enriched process logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 174–186. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Causal nets: a modeling language tailored towards process discovery. In: Katoen, J.-P., König, B. (eds.) CONCUR 2011. LNCS, vol. 6901, pp. 28–42. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Van Dongen, B.F.: BPI Challenge 2014. Rabobank Nederland. Dataset (2014).
  19. 19.
    Webb, G.I., Butler, S., Newlands, D.: On detecting differences between groups. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.QPR Software PlcHelsinkiFinland
  2. 2.Department of Computer Science, School of ScienceAalto UniversityEspooFinland

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