Advertisement

Comprehensive Process Drift Detection with Visual Analytics

  • Anton YeshchenkoEmail author
  • Claudio Di Ciccio
  • Jan Mendling
  • Artem Polyvyanyy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)

Abstract

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.

Keywords

Process mining Process drifts Declarative process models 

Notes

Acknowledgements

This work is partially funded by the EU H2020 program under MSCA-RISE agreement 645751 (RISE_BPM). Artem Polyvyanyy was partly supported by the Australian Research Council Discovery Project DP180102839.

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining - Data Science in Action. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. CS - R&D 23(2), 99–113 (2009)Google Scholar
  3. 3.
    Abanda, A., Mori, U., Lozano, J.A.: A review on distance based time series classification. DMKD 33(2), 378–412 (2019)MathSciNetGoogle Scholar
  4. 4.
    Aghabozorgi, S., Seyed Shirkhorshidi, A., Ying Wah, T.: Time-series clustering - a decade review. IS 53(C), 16–38 (2015)Google Scholar
  5. 5.
    Bauer, M., Senderovich, A., Gal, A., Grunske, L., Weidlich, M.: How much event data is enough? A statistical framework for process discovery. In: CAISE, pp. 239–256 (2018)Google Scholar
  6. 6.
    Truonga, C., Oudre, L., Vayatis, N.: Selective review of offline change point detection methods (2019). arxiv:1801.00718
  7. 7.
    Denisov, V., Belkina, E., Fahland, D.: BPIC 2018: Mining Concept Drift in Performance Spectra of Processes (2018)Google Scholar
  8. 8.
    Di Ciccio, C., Maggi, F.M., Mendling, J.: Efficient discovery of target-branched declare constraints. Inf. Syst. 56, 258–283 (2016)CrossRefGoogle Scholar
  9. 9.
    Di Ciccio, C., Maggi, F.M., Montali, M., Mendling, J.: Resolving inconsistencies and redundancies in declarative process models. IS 64, 425–446 (2017)Google Scholar
  10. 10.
    Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM TMIS 5(4), 24:1–24:37 (2015)Google Scholar
  11. 11.
    Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44:1–44:37 (2014)CrossRefGoogle Scholar
  12. 12.
    Hompes, B., Buijs, J.C.A.M., van der Aalst, W.M.P., Dixit, P., Buurman, H.: Detecting change in processes using comparative trace clustering. SIMPDA 2015, 95–108 (2015)Google Scholar
  13. 13.
    Killick, R., Fearnhead, P., Eckley, I.A.: Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107(500), 1590–1598 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Maaradji, A., Dumas, M., La Rosa, M., Ostovar, A.: Detecting sudden and gradual drifts in business processes from execution traces. IEEE TKDE 29(10), 2140–2154 (2017)Google Scholar
  15. 15.
    Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-guided discovery of declarative process models. In: CIDM, pp. 192–199. IEEE (2011)Google Scholar
  16. 16.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ostovar, A., Leemans, S.J., La Rosa, M.: Robust drift characterization from event streams of business processes (2018). https://eprints.qut.edu.au/121158/
  18. 18.
    Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M., van Dongen, B.F.: Detecting drift from event streams of unpredictable business processes. In: ER, pp. 330–346 (2016)Google Scholar
  19. 19.
    Poll, R., Polyvyanyy, A., Rosemann, M., Röglinger, M., Rupprecht, L.: Process forecasting: towards proactive business process management. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 496–512. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-98648-7_29CrossRefGoogle Scholar
  20. 20.
    Polyvyanyy, A., Armas-Cervantes, A., Dumas, M., García-Bañuelos, L.: On the expressive power of behavioral profiles. Formal Asp. Comput. 28(4), 597–613 (2016)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Polyvyanyy, A., Weidlich, M., Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: The 4C spectrum of fundamental behavioral relations for concurrent systems. In: Ciardo, G., Kindler, E. (eds.) PETRI NETS 2014. LNCS, vol. 8489, pp. 210–232. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07734-5_12CrossRefzbMATHGoogle Scholar
  22. 22.
    Seeliger, A., Nolle, T., Mühlhäuser, M.: Detecting concept drift in processes using graph metrics on process graphs. In: S-BPM, p. 6 (2017)Google Scholar
  23. 23.
    Tsymbal, A.: The problem of concept drift: definitions and related work. Comput. Sci. Depart. Trinity College Dublin 106(2), 58 (2004)Google Scholar
  24. 24.
    van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. TKDE 16(9), 1128–1142 (2004)Google Scholar
  25. 25.
    Ware, C.: Information visualization: perception for design. Elsevier, Amsterdam (2012)Google Scholar
  26. 26.
    Zheng, C., Wen, L., Wang, J.: Detecting process concept drifts from event logs. In: OTM CoopIS, pp. 524–542 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.The University of MelbourneParkvilleAustralia

Personalised recommendations