An Extensible Framework for Analysing Resource Behaviour Using Event Logs

  • Anastasiia Pika
  • Moe T. Wynn
  • Colin J. Fidge
  • Arthur H. M. ter Hofstede
  • Michael Leyer
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8484)


Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes’ executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework’s applicability is demonstrated using an event log from a German bank.


Process mining resource behaviour indicators employee performance measurements 


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  1. 1.
    Bose, J.C.R.P., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Transactions on Neural Networks and Learning Systems, PP(99) (2013)Google Scholar
  2. 2.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444 (1978)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Hawkins, D.M., Deng, Q.: A nonparametric change-point control chart. Journal of Quality Technology 42(2), 165–173 (2010)Google Scholar
  4. 4.
    Huang, Z., Lu, X., Duan, H.: Resource behavior measure and application in business process management. Expert Systems with Applications 39(7), 6458–6468 (2012)CrossRefGoogle Scholar
  5. 5.
    International Organization for Standardization /International Electrotechnical Commission. Information technology - Database languages - SQL (ISO/IEC 9075:2011) (2011)Google Scholar
  6. 6.
    Lijffijt, J., Papapetrou, P., Puolamäki, K.: Size matters: Finding the most informative set of window lengths. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 451–466. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics 18(1), 50–60 (1947)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Murphy, P.: Service performance measurement using simple techniques actually works. Journal of Marketing Practice: Applied Marketing Science 5(2), 56–73 (1999)CrossRefGoogle Scholar
  9. 9.
    Nakatumba, J., van der Aalst, W.M.P.: Analyzing resource behavior using process mining. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 69–80. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Neely, A., Gregory, M., Platts, K.: Performance measurement system design: a literature review and research agenda. International Journal of Operations & Production Management 25(12), 1228–1263 (2005)CrossRefGoogle Scholar
  11. 11.
    Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S.: State of the art literature review on performance measurement. Computers & Industrial Engineering 60(2), 279–290 (2011)CrossRefGoogle Scholar
  12. 12.
    Pika, A., van der Aalst, W.M.P., Fidge, C.J., ter Hofstede, A.H.M., Wynn, M.T.: Predicting deadline transgressions using event logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 211–216. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Pika, A., van der Aalst, W.M.P., Fidge, C.J., ter Hofstede, A.H.M., Wynn, M.T.: Profiling event logs to configure risk indicators for process delays. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 465–481. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Rao, D.S.P., O’Donnell, C.J., Battese, G.E., Coelli, T.J.: An introduction to efficiency and productivity analysis. Springer (2005)Google Scholar
  15. 15.
    Ross, G.J., Adams, N.M.: Two nonparametric control charts for detecting arbitrary distribution changes. Journal of Quality Technology 44(2), 102–116 (2012)Google Scholar
  16. 16.
    Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decision Support Systems 46(1), 300–317 (2008)CrossRefGoogle Scholar
  17. 17.
    Thevendran, V., Mawdesley, M.J.: Perception of human risk factors in construction projects: an exploratory study. International Journal of Project Management 22(2), 131–137 (2004)CrossRefGoogle Scholar
  18. 18.
    Thompson, G.M., Goodale, J.C.: Variable employee productivity in workforce scheduling. European Journal of Operational Research 170(2), 376–390 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    van der Aalst, W.M.P.: Business process simulation revisited. In: Barjis, J. (ed.) EOMAS 2010. LNBIP, vol. 63, pp. 1–14. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)CrossRefGoogle Scholar
  21. 21.
    van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering social networks from event logs. Computer Supported Cooperative Work (CSCW) 14(6), 549–593 (2005)CrossRefGoogle Scholar
  22. 22.
    van der Loo, M.P.J.: Distribution based outlier detection for univariate data: discussion paper 10003. Statistics Netherlands (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anastasiia Pika
    • 1
  • Moe T. Wynn
    • 1
  • Colin J. Fidge
    • 1
  • Arthur H. M. ter Hofstede
    • 1
    • 2
  • Michael Leyer
    • 3
  • Wil M. P. van der Aalst
    • 2
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Frankfurt School of Finance and ManagementFrankfurt am MainGermany

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