Comprehensive Process Drift Detection with Visual Analytics
Conference paper
First Online:
- 4 Citations
- 858 Downloads
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 modelsNotes
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.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.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.Abanda, A., Mori, U., Lozano, J.A.: A review on distance based time series classification. DMKD 33(2), 378–412 (2019)MathSciNetGoogle Scholar
- 4.Aghabozorgi, S., Seyed Shirkhorshidi, A., Ying Wah, T.: Time-series clustering - a decade review. IS 53(C), 16–38 (2015)Google Scholar
- 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.Truonga, C., Oudre, L., Vayatis, N.: Selective review of offline change point detection methods (2019). arxiv:1801.00718
- 7.Denisov, V., Belkina, E., Fahland, D.: BPIC 2018: Mining Concept Drift in Performance Spectra of Processes (2018)Google Scholar
- 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.Tsymbal, A.: The problem of concept drift: definitions and related work. Comput. Sci. Depart. Trinity College Dublin 106(2), 58 (2004)Google Scholar
- 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.Ware, C.: Information visualization: perception for design. Elsevier, Amsterdam (2012)Google Scholar
- 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