The ROAD from Sensor Data to Process Instances via Interaction Mining

  • Arik SenderovichEmail author
  • Andreas Rogge-Solti
  • Avigdor Gal
  • Jan Mendling
  • Avishai Mandelbaum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)


Process mining is a rapidly developing field that aims at automated modeling of business processes based on data coming from event logs. In recent years, advances in tracking technologies, e.g., Real-Time Locating Systems (RTLS), put forward the ability to log business process events as location sensor data. To apply process mining techniques to such sensor data, one needs to overcome an abstraction gap, because location data recordings do not relate to the process directly. In this work, we solve the problem of mapping sensor data to event logs based on process knowledge. Specifically, we propose interactions as an intermediate knowledge layer between the sensor data and the event log. We solve the mapping problem via optimal matching between interactions and process instances. An empirical evaluation of our approach shows its feasibility and provides insights into the relation between ambiguities and deviations from process knowledge, and accuracy of the resulting event log.


RTLS data Business processes Optimal matching Knowledge-driven 



This work was supported by the EU project SERAMIS (612052).


  1. 1.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications Co., Greenwich (2010)Google Scholar
  3. 3.
    Schrijver, A.: Theory of Linear and Integer Programming. Wiley, Chichester (1998)zbMATHGoogle Scholar
  4. 4.
    Leopold, H., Niepert, M., Weidlich, M., Mendling, J., Dijkman, R., Stuckenschmidt, H.: Probabilistic optimization of semantic process model matching. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 319–334. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. J. ACM 21(1), 168–173 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)CrossRefGoogle Scholar
  7. 7.
    Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)CrossRefGoogle Scholar
  8. 8.
    Baier, T., Mendling, J.: Bridging abstraction layers in process mining by automated matching of events and activities. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 17–32. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Baier, T., Rogge-Solti, A., Weske, M., Mendling, J.: Matching of events and activities - an approach based on constraint satisfaction. In: Frank, U., Loucopoulos, P., Pastor, Ó., Petrounias, I. (eds.) PoEM 2014. LNBIP, vol. 197, pp. 58–72. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Folino, F., Guarascio, M., Pontieri, L.: Mining predictive process models out of low-level multidimensional logs. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 533–547. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Günther, C.W., Rozinat, A., van der Aalst, W.M.P.: Activity mining by global trace segmentation. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 128–139. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Ferreira, D.R., Szimanski, F., Ralha, C.G.: Mining the low-level behaviour of agents in high-level business processes. Int. J. Bus. Process Integr. Manag. 6(2), 146–166 (2013)CrossRefGoogle Scholar
  13. 13.
    Mannhardt, F., de Leoni, M., Reijers, H., van der Aalst, W.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Dijkman, R., Dumas, M., Van Dongen, B., Käärik, R., Mendling, J.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)CrossRefGoogle Scholar
  15. 15.
    Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 42(6), 790–808 (2012)CrossRefGoogle Scholar
  16. 16.
    Artikis, A., Skarlatidis, A., Portet, F., Paliouras, G.: Logic-based event recognition. Knowl. Eng. Rev. 27(04), 469–506 (2012)CrossRefGoogle Scholar
  17. 17.
    Azkune, G., Almeida, A., López-de Ipiña, D., Chen, L.: Extending knowledge-driven activity models through data-driven learning techniques. Expert Syst. Appl. 42(6), 3115–3128 (2015)CrossRefGoogle Scholar
  18. 18.
    Han, Y., Tucker, C.S., Simpson, T.W., Davidson, E.: A data mining trajectory clustering methodology for modeling indoor design space utilization. In: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers V03BT03A017–V03BT03A028 (2013)Google Scholar
  19. 19.
    Liu, C., Ge, Y., Xiong, H., Xiao, K., Geng, W., Perkins, M.: Proactive workflow modeling by stochastic processes with application to healthcare operation and management. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 1593–1602. ACM, New York (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Arik Senderovich
    • 1
    Email author
  • Andreas Rogge-Solti
    • 2
  • Avigdor Gal
    • 1
  • Jan Mendling
    • 2
  • Avishai Mandelbaum
    • 1
  1. 1.Technion – Israel Institute of TechnologyHaifaIsrael
  2. 2.Vienna University of Economics and BusinessViennaAustria

Personalised recommendations