On the Contextualization of Event-Activity Mappings

  • Agnes KoschmiderEmail author
  • Felix Mannhardt
  • Tobias Heuser
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)


Event log files are used as input to any process mining algorithm. A main assumption of process mining is that each event has been assigned to a distinct process activity already. However, such mapping of events to activities is a considerable challenge. The current status-quo is that approaches indicate only likelihoods of mappings, since there is often more than one possible solution. To increase the quality of event to activity mappings this paper derives a contextualization for event-activity mappings and argues for a stronger consideration of contextual factors. Based on a literature review, the paper provides a framework for classifying context factors for event-activity mappings. We aim to apply this framework to improve the accuracy of event-activity mappings and, thereby, process mining results in scenarios with low-level events.


  1. 1.
    Soffer, P., et al.: From event streams to process models and back: challenges and opportunities. Information Systems (2018)Google Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). Scholar
  3. 3.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: Guided process discovery - a pattern-based approach. Inf. Syst. 76, 1–18 (2018)CrossRefGoogle Scholar
  4. 4.
    Günther, C.W.: Process Mining in Flexible Environments. PhD thesis, Technische Universiteit Eindhoven (2009)Google Scholar
  5. 5.
    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, Cham (2014). Scholar
  6. 6.
    Ferreira, D.R., Szimanski, F., Ralha, C.G.: Improving process models by mining mappings of low-level events to high-level activities. J. Intell. Inf. Syst. 43(2), 379–407 (2014)CrossRefGoogle Scholar
  7. 7.
    Eyers, D.M., Gal, A., Jacobsen, H., Weidlich, M.: Integrating process-oriented and event-based systems. Dagstuhl Rep. 6(8), 21–64 (2016)Google Scholar
  8. 8.
    van der Aa, H., Leopold, H., Reijers, H.A.: Checking process compliance on the basis of uncertain event-to-activity mappings. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 79–93. Springer, Cham (2017). Scholar
  9. 9.
    Dey, A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  10. 10.
    Trunko, R.: Kontextsensitive Ausnahmebehandlung in Geschftsprozessen. Verlag Dr. Hut (2011)Google Scholar
  11. 11.
    Bose, R.J.C., Van der Aalst, W.M.: Context aware trace clustering: towards improving process mining results. In: Proceedings of the 2009 SIAM International Conference on Data Mining, SIAM, pp. 401–412 (2009)Google Scholar
  12. 12.
    Rosemann, M., Recker, J.: Context-aware process design exploring the extrinsic drivers for process flexibility. In: BPMDS, CEUR Workshop Proceedings, vol. 236 (2006)Google Scholar
  13. 13.
    Zimmermann, A., Lorenz, A., Oppermann, R.: An operational definition of context. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS (LNAI), vol. 4635, pp. 558–571. Springer, Heidelberg (2007). Scholar
  14. 14.
    Mounira, Z., Mahmoud, B.: Context-aware process mining framework for business process flexibility. In: iiWAS 2010, pp. 421–426. ACM (2010)Google Scholar
  15. 15.
    Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 287–304. Springer, Heidelberg (2012). Scholar
  16. 16.
    Saidani, O., Nurcan, S.: Towards context aware business process modelling. In: BPMDS 2007 (2007)Google Scholar
  17. 17.
    Kofod-Petersen, A., Cassens, J.: Using activity theory to model context awareness. In: Roth-Berghofer, T.R., Schulz, S., Leake, D.B. (eds.) MRC 2005. LNCS (LNAI), vol. 3946, pp. 1–17. Springer, Heidelberg (2006). Scholar
  18. 18.
    Michael, J., Steinberger, C.: Context modeling for active assistance. In: ER Forum/Demos, CEUR Workshop Proceedings, vol. 1979, pp. 207–220 (2017)Google Scholar
  19. 19.
    Becker, T., Intoyoad, W.: Context aware process mining in logistics. Procedia CIRP 63, 557–562 (2017). Manufacturing Systems 4.0, Proceedings of the 50th CIRP Conference on Manufacturing SystemsCrossRefGoogle Scholar
  20. 20.
    Schnig, S., Cabanillas, C., Jablonski, S., Mendling, J.: A framework for efficiently mining the organisational perspective of business processes. Decis. Support Syst. 89, 87–97 (2016)CrossRefGoogle Scholar
  21. 21.
    Măruşter, L., Weijters, A.J.M.M.T., Van Der Aalst, W.M.P., Van Den Bosch, A.: A rule-based approach for process discovery: Dealing with noise and imbalance in process logs. Data Min. Knowl. Discov., 13(1), 67–87 (2006)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Hornung, T., Koschmider, A., Oberweis, A.: Rule-based auto completion of business process models. In: CAiSE Forum, CEUR Workshop Proceedings, vol. 247 (2007)Google Scholar
  23. 23.
    van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering social networks from event logs. Comput. Support. Coop. Work 14(6), 549–593 (2005)CrossRefGoogle Scholar
  24. 24.
    Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46(1), 300–317 (2008)CrossRefGoogle Scholar
  25. 25.
    Jin, T., Wang, J., Wen, L.: Organizational modeling from event logs. In: Sixth International Conference on Grid and Cooperative Computing, pp. 670–675 (2007)Google Scholar
  26. 26.
    Rinderle-Ma, S., Wil, M.: Life-cycle support for staff assignment rules in process-aware information systems. Technical report (2007)Google Scholar
  27. 27.
    Cheng, H.J., Kumar, A.: Process mining on noisy logs can log sanitization help to improve performance? Decis. Support Syst. 79, 138–149 (2015)CrossRefGoogle Scholar
  28. 28.
    Deneckère, R., Hug, C., Khodabandelou, G., Salinesi, C.: Intentional process mining: Discovering and modeling the goals behind processes using supervised learning. IJISMD 5(4), 22–47 (2014)Google Scholar
  29. 29.
    Koschmider, A., Song, M., Reijers, H.A.: Advanced social features in a recommendation system for process modeling. In: Abramowicz, W. (ed.) BIS 2009. LNBIP, vol. 21, pp. 109–120. Springer, Heidelberg (2009). Scholar
  30. 30.
    Caron, F., Vanthienen, J., Baesens, B.: Rule-based business process mining: applications for management. In: Management Intelligent Systems, vol. 171, pp. 273–282. Springer, Heidelberg (2012). Scholar
  31. 31.
    Schönig, S., Gillitzer, F., Zeising, M., Jablonski, S.: Supporting rule-based process mining by user-guided discovery of resource-aware frequent patterns. In: Toumani, F., et al. (eds.) ICSOC 2014. LNCS, vol. 8954, pp. 108–119. Springer, Cham (2015). Scholar
  32. 32.
    Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Event abstraction for process mining using supervised learning techniques. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 15, pp. 251–269. Springer, Cham (2018). Scholar
  33. 33.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). Scholar
  34. 34.
    Blank, P., Maurer, M., Siebenhofer, M., Rogge-Solti, A., Schonig, S.: Location-aware path alignment in process mining. EDOCW 2016, 1–8 (2016)Google Scholar
  35. 35.
    Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.M., Traver, V.: Process mining methodology for health process tracking using real-time indoor location systems. Sensors 15(12), 29821–29840 (2015)CrossRefGoogle Scholar
  36. 36.
    Koschmider, A., Reijers, H.A.: Improving the process of process modelling by the use of domain process patterns. Enterp. IS 9(1), 29–57 (2015)Google Scholar
  37. 37.
    Folino, F., Guarascio, M., Pontieri, L.: Miningmulti-variant process models from low-level logs. In: Abramowicz, W. (ed.) BIS 2015. LNBIP, vol. 208, pp. 165–177. Springer, Cham (2015). Scholar
  38. 38.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016). Scholar
  39. 39.
    Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)CrossRefGoogle Scholar
  40. 40.
    Tax, N., Alasgarov, E., Sidorova, N., Haakma, R.: On generation of time-based label refinements. arXiv preprint arXiv:1609.03333 (2016)
  41. 41.
    Diamantini, C., Genga, L., Potena, D.: Behavioral process mining for unstructured processes. J. Intell. Inf. Syst. 47(1), 5–32 (2016)CrossRefGoogle Scholar
  42. 42.
    Goedertier, S., Martens, D., Baesens, B., Haesen, R., Vanthienen, J.: A new approach for discovering business process models from event logs. Technical report, SSRN (2007)Google Scholar
  43. 43.
    Zang, C., Fan, Y.: Complex event processing in enterprise information systems based on RFID. Enterp. Inf. Syst. 1(1), 3–23 (2007)CrossRefGoogle Scholar
  44. 44.
    Alpers, S., Pilipchuk, R., Oberweis, A., Reussner, R.H.: Identifying needs for a holistic modelling approach to privacy aspects in enterprise software systems. ICISSP, SciTePress 18, 74–82 (2018)Google Scholar
  45. 45.
    Fazzinga, B., Flesca, S., Furfaro, F., Masciari, E., Pontieri, L.: Efficiently interpreting traces of low level events in business process logs. Inf. Syst. 73, 1–24 (2018)CrossRefGoogle Scholar
  46. 46.
    Tax, N., Sidorova, N., van der Aalst, W.M.P.: Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst., 1–33 (2018)Google Scholar
  47. 47.
    Lu, X., et al.: Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In: Panetto, H., et al. (eds.) On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science, vol. 10573, pp. 154–174. Springer, Cham (2017). Scholar
  48. 48.
    Begicheva, K., Lomazova, I.A.: Discovering high-level process models from event logs. Model. Anal. Inf. Syst. 24, 125–140 (2017)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Fazzinga, B., Flesca, S., Furfaro, F., Pontieri, L.: Online and offline classification of traces of event logs on the basis of security risks. J. Intell. Inf. Syst. 50(1), 195–230 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Agnes Koschmider
    • 1
    Email author
  • Felix Mannhardt
    • 2
  • Tobias Heuser
    • 1
  1. 1.Institute AIFBKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Department of Economics and Technology ManagementSINTEFTrondheimNorway

Personalised recommendations